An on-line method was developed to improve diagnostic accuracy and speed for analyzing running motors on site.On-line pre-measured data was used as the basis for constructing the membership functions used in a fuzzy neural network(FNN)as well as for network training to reduce the effects of various static factors,such as unbalanced input power and asymmetrical motor alignment,to increase accuracy. The preprocessed data and fuzzy logic were used to find the nonlinear mapping relationships between the data and the conclusions.The FNN was then constructed to carry motor fault diagnostics,which gives fast accurate diagnostics.The on-line fast motor fault diagnostics clearly indicate the fault type,location,and severity in running motors.This approach can also be extended to other applications. An on-line method was developed to improve diagnostic accuracy and speed for analyzing running motors on site. On-line pre-measured data was used as the basis for constructing the membership functions used in a fuzzy neural network (FNN) as well as for network training to reduce the effects of various static factors, such as unbalanced input power and asymmetrical motor alignment, to increase accuracy. The preprocessed data and fuzzy logic were used to find the nonlinear mapping relationships between the data and the conclusions. FNN was then constructed to carry motor fault diagnostics, which give fast accurate diagnostics. which on-line fast motor fault diagnostics clearly indicate the fault type, location, and severity in running motors. This approach can also be extended to other applications.