This paper presents a methodology for the development of software for classifying power system disturbances by type from the transient waveform signature. The implementation of classification capability in future transient recorders will enable such features as selective storage of transient data (to better utilize limited storage media) and automated reporting of disturbances to central control facilities. The wavelet transform provides an effective and efficient means of decomposing voltage and current signals of power system transients to detectable and discriminant features. Similarities of power system transients to wide-band signals in other domains, the simultaneous presence of a resonant frequency, its harmonics, and impulse (high-frequency, time-localized) components, make this technique extendible to other classification systems. The classification algorithm uses statistical pattern recognition on features derived from the extreme representation of the transient waveform after processing the transient waveform by a non-orthogonal, quadratic spline wavelet. Training and classification testing use simulated waveforms of a 200 mile, three-phase transmission line produced by the Electromagnetic Transients Program (EMTP). A simple Bayesian classifier identifies an unknown transient waveform as a capacitor switching or fault transient, and locates the point of disturbance from one of two possible locations on the transmission line. Due to the effectiveness of the wavelet transform preprocessing, the classification system currently performs at 100 percent accuracy on four transient classes.