Using a new method published by the first author, this article shows how direct explanations can be provided to interpret the classification of any input case by a standard multilayer perceptron (MLP) network. The method is demonstrated for a real-world MLP that classifies low-back-pain patients into three diagnostic classes. The application of the method leads to the discovery of a number of mis-diagnosed training and test cases and to the development of a more optimal low-back-pain MLP network.