A major requirement of any power apparatus is the reliable performance of its insulation. The incidence of minor flaws and irregularities such as voids, surface imperfections, in the electrical insulation is however inevitable and lead to partial discharge (PD). Since each defect has a unique degradation mechanism, it is imperative to ascertain the correlation between the discharge patterns and the type of defect in order to evaluate the quality of the insulation. Efforts to correlate discharge patterns with the type of defects have been undertaken by several researchers. Though encouraging attempts to recognize and classify simple PD defect sources have been reported, misclassifications still occur, which affect the assessment of the index of the insulating degradation. A Composite Probabilistic Neural Network Inference System has been devised and elucidated in this research using two versions of Probabilistic Neural Network. The inference is obtained based on the outcome to innovatively conceived fourteen unique characteristic vector inputs to enable an accurate and reliable decision in the classification of complex stochastic PD patterns thus obviating the necessity of skilled operators. Validation of the fingerprints of PD patterns has also been carried out using well-established techniques. © 2007 Elsevier Ltd. All rights reserved.