Developing the network for a truck requires extensive testing. The network consists of many different electronic control units and integration tests have to be carried out between them to make sure that the communication is working as specified. For this purpose a lab has been constructed so that the network can be tested in a controlled environment. These tests consume a lot of time and in order to finish them all on time test scripts have been developed to automate as much of the test process as possible. However there is still information that has to be manually fed into the system. The information that is of interest in this project is the visual information of the instrument cluster. This master thesis examines the possibility of using image analysis as a tool to further automate these test scripts by feeding the visual information in the ICL back to the test scripts. This project has resulted in a machine vision library that is capable of recording the visual state of the instrument cluster and a server that the test scripts are able to request information from.