Currently industrial applications require suitable monitoring systems able to identify any decrease in efficiency resulting in economic losses. This paper shows that the information coming from a general purpose monitoring system can be usefully exploited to realize a sensorless instrument for the monitoring of an ac motor drive, and can be fed to a diagnostic tool for providing useful risk coefficients. The method is based on digital processing of the line signals acquired by means of a virtual instrument. The employed wavelet algorithms have been implemented within a Matlab environment, and risk coefficients are generated by means of suitable neurofuzzy algorithms.