This letter investigates the effectiveness of a static neural network (NN) for monitoring of power electronic circuits. The NN is trained to form a mapping between the inputs and outputs of a power electronic circuit, which in this study is considered to be a full-bridge diode rectifier. Dynamic models have been used for the rectifier diodes. The ultimate objective of the designed NN is to provide an indication when the performance properties of one or more components in the rectifier circuit have changed from their original conditions-long before a noticeable degradation in the performance of the circuit or even a failure happens. Such information can be invaluable for many sensitive power electronic applications. The ideas put forth in this letter are not dependent on the type of the circuit and can be readily applied to more complex power electronic circuits.