This paper presents an effective approach to the fault diagnosis of pulsewidth modulated (PWM) DC-DC power converters. It is based on a multilayer multivalued neuron neural network with a complex QR decomposition. This network is used to identify the parameter values running out of tolerance range in two topologies of PWM DC-DC converters, namely, the buck and boost circuits. The proposed technique is based on measurements of steady-state voltages and currents waveforms.