This paper discusses the general application of using Neural Networks (NN) to classify six different types of Partial Discharge (PD). Stator winding failures contribute about 30-40% of the total motor failures according to IEEE and EPRI. Ninety percent (90%) of electrical failures on High-Voltage (HV) equipment are related to insulation deterioration. Large datasets were collected for motors with PD defects as well as PD-free machines. The datasets of PD were pre-processed and prepared for use with a NN using statistical means. It was possible to utilise the advantages offered by multiple NN models to classify the PD defects with a maximum recognition rate of 94.5% achieved, whereas previous research work did not exceed a classification accuracy of 79%.