diff --git a/spacy/cli/train.py b/spacy/cli/train.py
index 04aac8319..a3dcf9ebc 100644
--- a/spacy/cli/train.py
+++ b/spacy/cli/train.py
@@ -32,10 +32,12 @@ from ..compat import json_dumps
     resume=("Whether to resume training", "flag", "R", bool),
     no_tagger=("Don't train tagger", "flag", "T", bool),
     no_parser=("Don't train parser", "flag", "P", bool),
-    no_entities=("Don't train NER", "flag", "N", bool)
+    no_entities=("Don't train NER", "flag", "N", bool),
+    gold_preproc=("Use gold preprocessing", "flag", "G", bool),
 )
 def train(cmd, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0,
-          use_gpu=-1, resume=False, no_tagger=False, no_parser=False, no_entities=False):
+          use_gpu=-1, resume=False, no_tagger=False, no_parser=False, no_entities=False,
+          gold_preproc=False):
     """
     Train a model. Expects data in spaCy's JSON format.
     """
@@ -86,7 +88,7 @@ def train(cmd, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0,
                 i += 20
             with tqdm.tqdm(total=n_train_words, leave=False) as pbar:
                 train_docs = corpus.train_docs(nlp, projectivize=True,
-                                               gold_preproc=False, max_length=0)
+                                               gold_preproc=gold_preproc, max_length=0)
                 losses = {}
                 for batch in minibatch(train_docs, size=batch_sizes):
                     docs, golds = zip(*batch)
@@ -104,7 +106,7 @@ def train(cmd, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0,
                 scorer = nlp_loaded.evaluate(
                             corpus.dev_docs(
                                 nlp_loaded,
-                                gold_preproc=False))
+                                gold_preproc=gold_preproc))
                 acc_loc =(output_path / ('model%d' % i) / 'accuracy.json')
                 with acc_loc.open('w') as file_:
                     file_.write(json_dumps(scorer.scores))