In this article, the insulation fault detection of high-voltage motors by the artificial neural network algorithm is used. The proposed method can evaluate the status of operating motor without interrupting the normal operation. According to the measurement of partial discharge information, this research establishes the relationship of stator failures and pattern features. This study uses common high-voltage motor stator fault types to experimentally produce four types of stator test models with insulation defects; these models are compared with a healthy motor model. Through the learning of the artificial neural network, the experimental results show that the artificial neural network-based stator fault diagnosis system proposed in this article has a recognition rate as high as 90% when the conjugate gradient algorithm is used, and there are 20 neurons in the hidden layer.