diff --git a/bin/hyppopy_exe.py b/bin/hyppopy_exe.py
index ef0610e..c0f4383 100644
--- a/bin/hyppopy_exe.py
+++ b/bin/hyppopy_exe.py
@@ -1,69 +1,72 @@
 #!/usr/bin/env python
 #
 # DKFZ
 #
 #
 # Copyright (c) German Cancer Research Center,
 # Division of Medical and Biological Informatics.
 # All rights reserved.
 #
 # This software is distributed WITHOUT ANY WARRANTY; without
 # even the implied warranty of MERCHANTABILITY or FITNESS FOR
 # A PARTICULAR PURPOSE.
 #
 # See LICENSE.txt or http://www.mitk.org for details.
 #
 # Author: Sven Wanner (s.wanner@dkfz.de)
 
 
 from hyppopy.projectmanager import ProjectManager
 from hyppopy.workflows.unet_usecase.unet_usecase import unet_usecase
 from hyppopy.workflows.svc_usecase.svc_usecase import svc_usecase
 from hyppopy.workflows.randomforest_usecase.randomforest_usecase import randomforest_usecase
+from hyppopy.workflows.imageregistration_usecase.imageregistration_usecase import imageregistration_usecase
 
 
 import os
 import sys
 import argparse
 
 
 def print_warning(msg):
     print("\n!!!!! WARNING !!!!!")
     print(msg)
     sys.exit()
 
 
 def args_check(args):
     if not args.workflow:
         print_warning("No workflow specified, check --help")
     if not args.config:
         print_warning("Missing config parameter, check --help")
     if not os.path.isfile(args.config):
         print_warning(f"Couldn't find configfile ({args.config}), please check your input --config")
 
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(description='UNet Hyppopy UseCase Example Optimization.')
     parser.add_argument('-w', '--workflow', type=str,
                         help='workflow to be executed')
     parser.add_argument('-c', '--config', type=str, help='config filename, .xml or .json formats are supported.'
                                                          'pass a full path filename or the filename only if the'
                                                          'configfile is in the data folder')
 
     args = parser.parse_args()
     args_check(args)
 
     ProjectManager.read_config(args.config)
 
     if args.workflow == "svc_usecase":
         uc = svc_usecase()
     elif args.workflow == "randomforest_usecase":
         uc = randomforest_usecase()
     elif args.workflow == "unet_usecase":
         uc = unet_usecase()
+    elif args.workflow == "imageregistration_usecase":
+        uc = imageregistration_usecase()
     else:
         print("No workflow called {} found!".format(args.workflow))
         sys.exit()
 
     uc.run()
     print(uc.get_results())
diff --git a/hyppopy/workflows/imageregistration_usecase/__init__.py b/hyppopy/workflows/imageregistration_usecase/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/hyppopy/workflows/imageregistration_usecase/imageregistration_usecase.py b/hyppopy/workflows/imageregistration_usecase/imageregistration_usecase.py
new file mode 100644
index 0000000..993c72a
--- /dev/null
+++ b/hyppopy/workflows/imageregistration_usecase/imageregistration_usecase.py
@@ -0,0 +1,52 @@
+# DKFZ
+#
+#
+# Copyright (c) German Cancer Research Center,
+# Division of Medical and Biological Informatics.
+# All rights reserved.
+#
+# This software is distributed WITHOUT ANY WARRANTY; without
+# even the implied warranty of MERCHANTABILITY or FITNESS FOR
+# A PARTICULAR PURPOSE.
+#
+# See LICENSE.txt or http://www.mitk.org for details.
+#
+# Author:
+
+#------------------------------------------------------
+# this needs to be imported, dont remove these
+from hyppopy.projectmanager import ProjectManager
+from hyppopy.workflows.workflowbase import WorkflowBase
+#------------------------------------------------------
+
+# import your external packages
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.model_selection import cross_val_score
+
+# import your custom DataLoader
+from hyppopy.workflows.dataloader.simpleloader import SimpleDataLoaderBase # This is a dataloader class create your own
+
+
+class imageregistration_usecase(WorkflowBase):
+
+    def setup(self):
+        # here you create your own DataLoader instance
+        dl = SimpleDataLoaderBase()
+        # call the start function of your DataLoader
+        dl.start(path=ProjectManager.data_path,
+                 data_name=ProjectManager.data_name,
+                 labels_name=ProjectManager.labels_name)
+        # pass the data to the solver
+        self.solver.set_data(dl.data)
+
+    def blackbox_function(self, data, params):
+        # converting number back to integers is an ugly hack that will be removed in the future
+        if "n_estimators" in params.keys():
+            params["n_estimators"] = int(round(params["n_estimators"]))
+
+        # Do your training
+        clf = RandomForestClassifier(**params)
+        # compute your loss
+        loss = -cross_val_score(estimator=clf, X=data[0], y=data[1], cv=3).mean()
+        # return loss
+        return loss