diff --git a/sklearn/metrics/pairwise.py b/sklearn/metrics/pairwise.py
index 4e82328f6fc53..0fa3ad793524a 100644
--- a/sklearn/metrics/pairwise.py
+++ b/sklearn/metrics/pairwise.py
@@ -11,6 +11,7 @@
 
 import itertools
 from functools import partial
+import warnings
 
 import numpy as np
 from scipy.spatial import distance
@@ -467,7 +468,7 @@ def pairwise_distances_argmin(X, Y, axis=1, metric="euclidean",
 
 
 def manhattan_distances(X, Y=None, sum_over_features=True,
-                        size_threshold=5e8):
+                        size_threshold=None):
     """ Compute the L1 distances between the vectors in X and Y.
 
     With sum_over_features equal to False it returns the componentwise
@@ -520,6 +521,10 @@ def manhattan_distances(X, Y=None, sum_over_features=True,
     array([[ 1.,  1.],
            [ 1.,  1.]]...)
     """
+    if size_threshold is not None:
+        warnings.warn('Use of the "size_threshold" is deprecated '
+                      'in 0.19 and it will be removed version '
+                      '0.21 of scikit-learn', DeprecationWarning)
     X, Y = check_pairwise_arrays(X, Y)
 
     if issparse(X) or issparse(Y):
diff --git a/sklearn/metrics/tests/test_pairwise.py b/sklearn/metrics/tests/test_pairwise.py
index d8b64b58ca481..242523034e7af 100644
--- a/sklearn/metrics/tests/test_pairwise.py
+++ b/sklearn/metrics/tests/test_pairwise.py
@@ -12,6 +12,7 @@
 from sklearn.utils.testing import assert_raises
 from sklearn.utils.testing import assert_raises_regexp
 from sklearn.utils.testing import assert_true
+from sklearn.utils.testing import assert_warns
 from sklearn.utils.testing import ignore_warnings
 
 from sklearn.externals.six import iteritems
@@ -74,10 +75,10 @@ def test_pairwise_distances():
     assert_equal(S.shape[0], X.shape[0])
     assert_equal(S.shape[1], Y.shape[0])
     assert_array_almost_equal(S, S2)
-    # Low-level function for manhattan can divide in blocks to avoid
-    # using too much memory during the broadcasting
-    S3 = manhattan_distances(X, Y, size_threshold=10)
-    assert_array_almost_equal(S, S3)
+    # Using size_threshold argument should raise
+    # a deprecation warning
+    assert_warns(DeprecationWarning,
+                 manhattan_distances, X, Y, size_threshold=10)
     # Test cosine as a string metric versus cosine callable
     # The string "cosine" uses sklearn.metric,
     # while the function cosine is scipy.spatial