diff --git a/sklearn/naive_bayes.py b/sklearn/naive_bayes.py
index 276ba53d91200..534acc420af66 100644
--- a/sklearn/naive_bayes.py
+++ b/sklearn/naive_bayes.py
@@ -286,6 +286,12 @@ def _count(X, Y):
             N_c is the count of all features in all samples of class c;
             N_c_i is the count of feature i in all samples of class c.
         """
+        if issparse(X):
+            if np.any(X.data < 0):
+                raise ValueError("Input X must be non-negative.")
+        else:
+            if np.any(X < 0):
+                raise ValueError("Input X must be non-negative.")
         N_c_i = safe_sparse_dot(Y.T, X)
         N_c = np.sum(N_c_i, axis=1)
 
diff --git a/sklearn/tests/test_naive_bayes.py b/sklearn/tests/test_naive_bayes.py
index b90e77a1e3d59..4028595c46049 100644
--- a/sklearn/tests/test_naive_bayes.py
+++ b/sklearn/tests/test_naive_bayes.py
@@ -73,6 +73,7 @@ def test_mnnb():
     for X in [X2, scipy.sparse.csr_matrix(X2)]:
         # Check the ability to predict the learning set.
         clf = MultinomialNB()
+        assert_raises(ValueError, clf.fit, -X, y2)
         y_pred = clf.fit(X, y2).predict(X)
 
         assert_array_equal(y_pred, y2)