diff --git a/tools/analyze_results.py b/tools/analyze_results.py
new file mode 100644
index 000000000..12a76a364
--- /dev/null
+++ b/tools/analyze_results.py
@@ -0,0 +1,99 @@
+import argparse
+import os.path as osp
+
+import mmcv
+from mmcv import DictAction
+
+from mmcls.datasets import build_dataset
+from mmcls.models import build_classifier
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='MMCls evaluate prediction success/fail')
+    parser.add_argument('config', help='test config file path')
+    parser.add_argument('result', help='test result json/pkl file')
+    parser.add_argument('--out-dir', help='dir to store output files')
+    parser.add_argument(
+        '--topk',
+        default=20,
+        type=int,
+        help='Number of images to select for sucess/fail')
+    parser.add_argument(
+        '--options',
+        nargs='+',
+        action=DictAction,
+        help='override some settings in the used config, the key-value pair '
+        'in xxx=yyy format will be merged into config file.')
+    args = parser.parse_args()
+    return args
+
+
+def save_imgs(result_dir, folder_name, results, model):
+    full_dir = osp.join(result_dir, folder_name)
+    mmcv.mkdir_or_exist(full_dir)
+    mmcv.dump(results, osp.join(full_dir, folder_name + '.json'))
+
+    # save imgs
+    show_keys = ['pred_score', 'pred_class', 'gt_class']
+    for result in results:
+        result_show = dict((k, v) for k, v in result.items() if k in show_keys)
+        outfile = osp.join(full_dir, osp.basename(result['filename']))
+        model.show_result(result['filename'], result_show, out_file=outfile)
+
+
+def main():
+    args = parse_args()
+
+    cfg = mmcv.Config.fromfile(args.config)
+    if args.options is not None:
+        cfg.merge_from_dict(args.options)
+
+    model = build_classifier(cfg.model)
+
+    # build the dataloader
+    dataset = build_dataset(cfg.data.test)
+    filenames = list()
+    for info in dataset.data_infos:
+        if info['img_prefix'] is not None:
+            filename = osp.join(info['img_prefix'],
+                                info['img_info']['filename'])
+        else:
+            filename = info['img_info']['filename']
+        filenames.append(filename)
+    gt_labels = list(dataset.get_gt_labels())
+    gt_classes = [dataset.CLASSES[x] for x in gt_labels]
+
+    # load test results
+    outputs = mmcv.load(args.result)
+    outputs['filename'] = filenames
+    outputs['gt_label'] = gt_labels
+    outputs['gt_class'] = gt_classes
+
+    outputs_list = list()
+    for i in range(len(gt_labels)):
+        output = dict()
+        for k in outputs.keys():
+            output[k] = outputs[k][i]
+        outputs_list.append(output)
+
+    # sort result
+    outputs_list = sorted(outputs_list, key=lambda x: x['pred_score'])
+
+    success = list()
+    fail = list()
+    for output in outputs_list:
+        if output['pred_label'] == output['gt_label']:
+            success.append(output)
+        else:
+            fail.append(output)
+
+    success = success[:args.topk]
+    fail = fail[:args.topk]
+
+    save_imgs(args.out_dir, 'success', success, model)
+    save_imgs(args.out_dir, 'fail', fail, model)
+
+
+if __name__ == '__main__':
+    main()