A method for testing a network is disclosed, the network having a number of network sections, in particular in a cable harness having a number of such networks, having the following steps of: recording training measured values for a number of reference networks, wherein the reference networks correspond to the network to be tested, preprocessing the recorded training measured values in order to eliminate data errors in the training measured values, training a first classification system using the training measured values, wherein the first classification system is based on at least one algorithm from the field of machine learning and is designed to classify a network either as fault-free or faulty, training a second classification system using the training measured values, wherein the second classification system is based on at least one algorithm from the field of machine learning and is designed to classify a faulty network section of a network, recording test measured values for the network to be tested, preprocessing the recorded test measured values in order to eliminate data errors in the training measured values, classifying the network to be tested as fault-free or faulty on the basis of the recorded test measured values using the trained first classification system, and classifying the faulty network section of the network to be tested using the trained second classification system if the network (to be tested was classified as faulty by the trained first classification system. The present invention also discloses a corresponding testing device.