diff --git a/benchmarks/bench_text_vectorizers.py b/benchmarks/bench_text_vectorizers.py
new file mode 100644
index 0000000000000..194eba7ea2e82
--- /dev/null
+++ b/benchmarks/bench_text_vectorizers.py
@@ -0,0 +1,76 @@
+"""
+
+To run this benchmark, you will need,
+
+ * scikit-learn
+ * pandas
+ * memory_profiler
+ * psutil (optional, but recommended)
+
+"""
+
+from __future__ import print_function
+
+import timeit
+import itertools
+
+import numpy as np
+import pandas as pd
+from memory_profiler import memory_usage
+
+from sklearn.datasets import fetch_20newsgroups
+from sklearn.feature_extraction.text import (CountVectorizer, TfidfVectorizer,
+                                             HashingVectorizer)
+
+n_repeat = 3
+
+
+def run_vectorizer(Vectorizer, X, **params):
+    def f():
+        vect = Vectorizer(**params)
+        vect.fit_transform(X)
+    return f
+
+
+text = fetch_20newsgroups(subset='train').data
+
+print("="*80 + '\n#' + "    Text vectorizers benchmark" + '\n' + '='*80 + '\n')
+print("Using a subset of the 20 newsrgoups dataset ({} documents)."
+      .format(len(text)))
+print("This benchmarks runs in ~20 min ...")
+
+res = []
+
+for Vectorizer, (analyzer, ngram_range) in itertools.product(
+            [CountVectorizer, TfidfVectorizer, HashingVectorizer],
+            [('word', (1, 1)),
+             ('word', (1, 2)),
+             ('word', (1, 4)),
+             ('char', (4, 4)),
+             ('char_wb', (4, 4))
+             ]):
+
+    bench = {'vectorizer': Vectorizer.__name__}
+    params = {'analyzer': analyzer, 'ngram_range': ngram_range}
+    bench.update(params)
+    dt = timeit.repeat(run_vectorizer(Vectorizer, text, **params),
+                       number=1,
+                       repeat=n_repeat)
+    bench['time'] = "{:.2f} (+-{:.2f})".format(np.mean(dt), np.std(dt))
+
+    mem_usage = memory_usage(run_vectorizer(Vectorizer, text, **params))
+
+    bench['memory'] = "{:.1f}".format(np.max(mem_usage))
+
+    res.append(bench)
+
+
+df = pd.DataFrame(res).set_index(['analyzer', 'ngram_range', 'vectorizer'])
+
+print('\n========== Run time performance (sec) ===========\n')
+print('Computing the mean and the standard deviation '
+      'of the run time over {} runs...\n'.format(n_repeat))
+print(df['time'].unstack(level=-1))
+
+print('\n=============== Memory usage (MB) ===============\n')
+print(df['memory'].unstack(level=-1))