Method for testing a network (151, 360, 461) comprising a number of network sections (A - I, J - Q), in particular in a wiring harness comprising a number of such networks (151, 360, 461), comprising the following steps: recording training measurement values for a number of reference networks, the reference networks corresponding to the network (151, 360, 461) to be tested, preprocessing the recorded training measurement values in order to eliminate data errors in the training measurement values, training a first classification system (104, 204) with the training measurement values, the first classification system (104, 204) being based on at least one algorithm from the field of machine learning and being designed to classify a network (151, 360, 461) as either fault-free or faulty, training a second classification system (105, 205) with the training measurement values, the second classification system (105, 205) being based on at least one algorithm from the field of machine learning and being designed to classify a faulty network section (A - I, J - Q) of a network (151, 360, 461), recording test measurement values for the network (151, 360, 461) to be tested, preprocessing the recorded test measurement values in order to eliminate data errors in the training measurement values, classifying the network (151, 360, 461) to be tested as fault-free or faulty on the basis of the recorded test measurement values with the trained first classification system (104, 204), and classifying the faulty network section (A - I, J - Q) of the network (151, 360, 461) to be tested with the trained second classification system (105, 205) if the network (151, 360, 461) to be tested was classified as faulty by the trained first classification system (104, 204). recording training measurement values for a number of reference networks, the reference networks corresponding to the network (151, 360, 461) to be tested, preprocessing the recorded training measurement values in order to eliminate data errors in the training measurement values, training a first classification system (104, 204) with the training measurement values, the first classification system (104, 204) being based on at least one algorithm from the field of machine learning and being designed to classify a network (151, 360, 461) as either fault-free or faulty, training a second classification system (105, 205) with the training measurement values, the second classification system (105, 205) being based on at least one algorithm from the field of machine learning and being designed to classify a faulty network section (A - I, J - Q) of a network (151, 360, 461), recording test measurement values for the network (151, 360, 461) to be tested, preprocessing the recorded test measurement values in order to eliminate data errors in the training measurement values, classifying the network (151, 360, 461) to be tested as fault-free or faulty on the basis of the recorded test measurement values with the trained first classification system (104, 204), and classifying the faulty network section (A - I, J - Q) of the network (151, 360, 461) to be tested with the trained second classification system (105, 205) if the network (151, 360, 461) to be tested was classified as faulty by the trained first classification system (104, 204).