Abstract: A gamut of insulation diagnostic methods is being practiced. Amongst them Partial Discharge (PD) detection, measurement and analysis is an inherently non pervasive-test test procedure. Hence, it is being considered as a crucial methodology. Over the last three decades attempts were made to discriminate single and partially overlapped PD sources have yielded moderate success. In the above process techniques like Fractal Features, Mixed Weibull Function, Neural Networks (NN) and Wavelet Transformation have been implemented. However, intricacies involved in discriminating abstruse overlapped signatures, aspects concerning training of neural networks for large and ill-conditioned data, complications related to varying applied voltages during measurement etc., continue to confront the research community. Since schemes for large dataset training based on arbitrarily chosen centers are found to be rather impractical and not tenable during discrimination, mixture density clustering technique that utilizes an Expectation Maximization with Maximum Likelihood strategy is implemented for training Homoscedastic and Heteroscedastic Probabilistic Neural Network (PNN) variants. Detailed analysis of the ability of the PNN variants is performed to determine the proposition of utilizing various preprocessing techniques in discriminating the PD signatures. In addition, studies are carried out on the PNN variants to determine the ability of the deterministically and autonomously created Probability Density Functions (PDF) in recognition and classification of substantially big dataset multi-source PD fingerprints due to varying levels of applied voltages.