This paper presents a neural network approach for machine fault diagnosis. Specifically, two tasks are explained and discussed: (1) a neural network-based machine fault diagnosis model is developed using the back propagation (BP) learning paradigm; (2) network training efficiency is studied by varying the learning rate and learning momentum of the activation function. The results are presented and discussed.