diff --git a/experiments/pairwise_benchmark.py b/experiments/benchmark.py
similarity index 85%
rename from experiments/pairwise_benchmark.py
rename to experiments/benchmark.py
index e6ffa81..77fcdbb 100644
--- a/experiments/pairwise_benchmark.py
+++ b/experiments/benchmark.py
@@ -3,7 +3,7 @@ from functools import partial
 from timeit import default_timer as timer
 from fog.clustering import *
 from fog.tokenizers import ngrams
-from fog.key import fingerprint
+from fog.key import fingerprint, omission_key
 from Levenshtein import distance as levenshtein
 
 with open('./data/universities.csv', 'r') as f:
@@ -33,6 +33,10 @@ with open('./data/universities.csv', 'r') as f:
     clusters = list(blocking(universities, blocks=partial(ngrams, 6), distance=levenshtein, radius=2))
     print('Blocking (%i):' % len(clusters), timer() - start)
 
+    start = timer()
+    clusters = list(sorted_neighborhood(universities, key=omission_key, distance=levenshtein, radius=2))
+    print('SNM Omission (%i):' % len(clusters), timer() - start)
+
 print()
 with open('./data/musicians.csv', 'r') as f:
     reader = csv.DictReader(f)
@@ -53,6 +57,10 @@ with open('./data/musicians.csv', 'r') as f:
     clusters = list(blocking(artists, blocks=partial(ngrams, 6), distance=levenshtein, radius=2, processes=8))
     print('Blocking (%i):' % len(clusters), timer() - start)
 
+    start = timer()
+    clusters = list(sorted_neighborhood(artists, key=omission_key, distance=levenshtein, radius=2))
+    print('SNM Omission (%i):' % len(clusters), timer() - start)
+
     start = timer()
     clusters = list(pairwise_fuzzy_clusters(artists, distance=levenshtein, radius=2, processes=8))
     print('Parallel Fuzzy clusters (%i):' % len(clusters), timer() - start)
diff --git a/fog/clustering/__init__.py b/fog/clustering/__init__.py
index edcbcc0..0eaea75 100644
--- a/fog/clustering/__init__.py
+++ b/fog/clustering/__init__.py
@@ -10,4 +10,5 @@ from fog.clustering.pairwise import (
     pairwise_fuzzy_clusters,
     pairwise_connected_components
 )
+from fog.clustering.sorted_neighborhood import sorted_neighborhood
 from fog.clustering.vp_tree import vp_tree
diff --git a/fog/clustering/blocking.py b/fog/clustering/blocking.py
index b46ad90..6773a43 100644
--- a/fog/clustering/blocking.py
+++ b/fog/clustering/blocking.py
@@ -44,8 +44,8 @@ def blocking_worker(payload):
 def blocking(data, block=None, blocks=None, similarity=None, distance=None,
              radius=None, min_size=2, max_size=float('inf'), processes=1):
     """
-    Function returning an iterator over found clusters using the leader
-    algorithm.
+    Function returning an iterator over found clusters using the blocking
+    method.
 
     It works by dispatching given items into one or more buckets before
     computing pairwise comparisons on each bucket.
diff --git a/fog/clustering/sorted_neighborhood.py b/fog/clustering/sorted_neighborhood.py
new file mode 100644
index 0000000..419a1a3
--- /dev/null
+++ b/fog/clustering/sorted_neighborhood.py
@@ -0,0 +1,84 @@
+# =============================================================================
+# Fog Sorted Neighborhood Clustering
+# =============================================================================
+#
+# Implementation of the Sorted Neighborhood method.
+#
+from collections import defaultdict
+from fog.clustering.utils import make_similarity_function
+
+
+def sorted_neighborhood(data, key=None, similarity=None, distance=None,
+                        radius=None, window=10, min_size=2, max_size=float('inf')):
+    """
+    Function returning an iterator over found clusters using the sorted
+    neighborhood method.
+
+    It works by first sorting the data according to a key which could, if
+    cleverly chosen, put similar items next to one another in the result.
+
+    We then attempt to find clusters by computing pairwise similarity/distances
+    in small blocks of constant size in the sorted list.
+
+    Omission key & skeleton keys by Pollock & Zamora are a good choice of
+    sorting key if you try to find mispellings, for instance.
+
+    Args:
+        data (iterable): Arbitrary iterable containing data points to gather
+            into clusters. Will be fully consumed.
+        key (callable, optional): key on which to sort the data.
+        similarity (callable): If radius is specified, a function returning
+            the similarity between two points. Else, a function returning
+            whether two points should be deemed similar. Alternatively, one can
+            specify `distance` instead.
+        distance (callable): If radius is specified, a function returning
+            the distance between two points. Else, a function returning
+            whether two point should not be deemed similar. Alternatively, one
+            can specify `similarity` instead.
+        radius (number, optional): produced clusters' radius.
+        window (number, optional): Size of the window in which to look for
+            matches. Defaults to 10.
+        min_size (number, optional): minimum number of items in a cluster for
+            it to be considered viable. Defaults to 2.
+        max_size (number, optional): maximum number of items in a cluster for
+            it to be considered viable. Defaults to infinity.
+
+    Yields:
+        list: A viable cluster.
+
+    """
+
+    # Formatting similarity
+    similarity = make_similarity_function(similarity=similarity, distance=distance, radius=radius)
+
+    # Iterating over sorted data
+    S = sorted(data, key=key)
+    n = len(S)
+
+    graph = defaultdict(list)
+
+    for i in range(n):
+        A = S[i]
+
+        for j in range(i + 1, min(n, i + window + 1)):
+            B = S[j]
+
+            if similarity(A, B):
+                graph[i].append(j)
+                graph[j].append(i)
+
+    # Building clusters
+    visited = set()
+    for i, neighbors in graph.items():
+        if i in visited:
+            continue
+
+        if len(neighbors) + 1 < min_size:
+            continue
+        if len(neighbors) + 1 > max_size:
+            continue
+
+        visited.update(neighbors)
+
+        cluster = [S[i]] + [S[j] for j in neighbors]
+        yield cluster
diff --git a/fog/key/omission.py b/fog/key/omission.py
index 952f370..96287ac 100644
--- a/fog/key/omission.py
+++ b/fog/key/omission.py
@@ -19,8 +19,6 @@ UNDESIRABLES_RE = re.compile(r'[^A-Z]')
 CONSONANTS = 'JKQXZVWYBFMGPDHCLNTSR'
 VOWELS = set('AEIOU')
 
-# TODO: omission/skeleton key clustering for distance = 1
-
 
 def omission_key(string):
     """
diff --git a/test/clustering/blocking_test.py b/test/clustering/blocking_test.py
index d473a4c..c1a2c13 100644
--- a/test/clustering/blocking_test.py
+++ b/test/clustering/blocking_test.py
@@ -5,7 +5,6 @@ import csv
 from test.clustering.utils import Clusters
 from Levenshtein import distance as levenshtein
 from fog.clustering import blocking
-from fog.tokenizers import ngrams
 
 DATA = [
     'Abelard',
diff --git a/test/clustering/sorted_neighborhood_test.py b/test/clustering/sorted_neighborhood_test.py
new file mode 100644
index 0000000..00056a8
--- /dev/null
+++ b/test/clustering/sorted_neighborhood_test.py
@@ -0,0 +1,31 @@
+# =============================================================================
+# Fog Sorted Neighborhood Unit Tests
+# =============================================================================
+import csv
+from test.clustering.utils import Clusters
+from Levenshtein import distance as levenshtein
+from fog.clustering import sorted_neighborhood
+
+DATA = [
+    'Abelard',
+    'Abelar',
+    'Atrium',
+    'Atrides',
+    'Belgian',
+    'Belgia',
+    'Telgia'
+]
+
+CLUSTERS = Clusters([
+    ('Abelard', 'Abelar'),
+    ('Belgian', 'Belgia')
+])
+
+
+class TestSortedNeighborhood(object):
+    def test_basics(self):
+
+        # Sorting alphabetically
+        clusters = Clusters(sorted_neighborhood(DATA, distance=levenshtein, radius=1, window=1))
+
+        assert clusters == CLUSTERS