diff --git a/examples/train_pos_tagger.py b/examples/train_pos_tagger.py
new file mode 100644
index 000000000..b1750ea64
--- /dev/null
+++ b/examples/train_pos_tagger.py
@@ -0,0 +1,70 @@
+"""A quick example for training a part-of-speech tagger, without worrying
+about the tokenization, or other language-specific customizations."""
+
+from __future__ import unicode_literals
+from __future__ import print_function
+
+import plac
+from os import path
+import os
+
+from spacy.vocab import Vocab
+from spacy.tokenizer import Tokenizer
+from spacy.tagger import Tagger
+import random
+
+
+# You need to define a mapping from your data's part-of-speech tag names to the
+# Universal Part-of-Speech tag set, as spaCy includes an enum of these tags.
+# See here for the Universal Tag Set:
+# http://universaldependencies.github.io/docs/u/pos/index.html
+# You may also specify morphological features for your tags, from the universal
+# scheme.
+TAG_MAP = {
+        'N': {"pos": "NOUN"},
+        'V': {"pos": "VERB"},
+        'J': {"pos": "ADJ"}
+    }
+
+# Usually you'll read this in, of course. Data formats vary.
+# Ensure your strings are unicode.
+DATA = [
+    (
+        ["I", "like", "green", "eggs"],
+        ["N", "V",    "J",     "N"]
+    ),
+    (
+        ["Eat", "blue", "ham"],
+        ["V",   "J",    "N"]
+    )
+]
+    
+def ensure_dir(*parts):
+    path_ = path.join(*parts)
+    if not path.exists(path_):
+        os.mkdir(path_)
+    return path_
+
+
+def main(output_dir):
+    ensure_dir(output_dir)
+    ensure_dir(output_dir, "pos")
+    ensure_dir(output_dir, "vocab")
+    
+    vocab = Vocab(tag_map=TAG_MAP)
+    tokenizer = Tokenizer(vocab, {}, None, None, None)
+    # The default_templates argument is where features are specified. See
+    # spacy/tagger.pyx for the defaults.
+    tagger = Tagger.blank(vocab, Tagger.default_templates())
+
+    for i in range(5):
+        for words, tags in DATA:
+            tokens = tokenizer.tokens_from_list(words)
+            tagger.train(tokens, tags)
+        random.shuffle(DATA)
+    tagger.model.end_training(path.join(output_dir, 'pos', 'model'))
+    vocab.strings.dump(path.join(output_dir, 'vocab', 'strings.txt'))
+
+
+if __name__ == '__main__':
+    plac.call(main)