diff --git a/doc/source/graph-run_time_meta.png b/doc/source/graph-run_time_meta.png
new file mode 100644
index 0000000..07f5ad1
Binary files /dev/null and b/doc/source/graph-run_time_meta.png differ
diff --git a/doc/source/graph.rst b/doc/source/graph.rst
index a9b98b6..ea41b21 100644
--- a/doc/source/graph.rst
+++ b/doc/source/graph.rst
@@ -137,6 +137,27 @@ will generate a graph like:
 
 .. image:: graph-dailycount.png
 
+Run Time Grouped By Run Metadata
+--------------------------------
+
+This graph generates a box and whiskers plot to show the distribution of run
+times for runs with each plot grouped by values to a provided run_metadata key
+
+For example, running something like::
+
+  subunit2sql-graph --title "Run times by Job Name" --database-connection mysql://test:test@localhost/subunit2sql -o test.png run_time_meta build_name --filter_list gate-tempest-dsvm-neutron-full-ssh,gate-tempest-dsvm-full-ubuntu-xenial,gate-tempest-dsvm-full-ubuntu-trusty,gate-tempest-dsvm-py35-ubuntu-xenial
+
+will generate a graph like:
+
+.. image:: graph-run_time_meta.png
+   :width: 115%
+
+It's also worth noting the --filter_list argument used in the command above.
+In some cases, especially larger data sets, there are too many distinct values
+for a given run_metadata key to make the graph useful. To workaround this you
+specify a list of values that will be used to filter the output so that only
+matches of that key will be in the output.
+
 subunit2sql-graph plugin interface
 ==================================
 
diff --git a/subunit2sql/analysis/run_time_meta.py b/subunit2sql/analysis/run_time_meta.py
index 0b533fa..561329c 100644
--- a/subunit2sql/analysis/run_time_meta.py
+++ b/subunit2sql/analysis/run_time_meta.py
@@ -28,10 +28,16 @@ matplotlib.style.use('ggplot')
 def set_cli_opts(parser):
     parser.add_argument('metadata_key',
                         help="The run_metadata key to group the runs by")
+    parser.add_argument('--filter_list', '-f',
+                        help='A comma seperated list of values to use')
 
 
 def generate_series():
     session = api.get_session()
+    if CONF.command.filter_list:
+        filter_list = CONF.command.filter_list.split(',')
+    else:
+        filter_list = None
     if CONF.start_date:
         start_date = datetime.datetime.strptime(CONF.start_date, '%Y-%m-%d')
     else:
@@ -43,8 +49,13 @@ def generate_series():
     run_times = api.get_run_times_grouped_by_run_metadata_key(
         CONF.command.metadata_key, start_date=start_date,
         stop_date=stop_date, session=session)
-    df = pd.DataFrame(dict(
-        [(k, pd.Series(v)) for k, v in run_times.iteritems()]))
+    if not filter_list:
+        df = pd.DataFrame(dict(
+            [(k, pd.Series(v)) for k, v in run_times.iteritems()]))
+    else:
+        df = pd.DataFrame(dict(
+            [(k, pd.Series(v)) for k, v in run_times.iteritems()
+             if k in filter_list]))
     if not CONF.title:
         title = "Run aggregate run time grouped by metadata"
     else: