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Results of investigations performed to evaluate the effectiveness of a new inference method for the diagnosis of solid insulation systems, based on partial discharge (PD) measurements, are reported in this paper. Signal separation, noise recognition, and PD source identification are the main features of the proposed inference method. Techniques for signal separation and automatic noise rejection are reported in the 1st part of this paper, while the problem of the identification of PD phenomena, occurring in defects of insulation systems, is approached in this 2nd part. The identification is based on fuzzy logic and enables the recognition of PD generated from different basic sources, such as internal, surface and corona discharges. It is shown that the different source typologies can be identified by means of fuzzy rules applied to a selection of parameters derived from PD-pulse phase and amplitude distribution analysis, once PD phenomena have been clustered in homogeneous class through a fuzzy algorithm based on PD-pulse shape. The proposed identification procedure is finally applied to rotating machines and cables, affected by insulation defects, showing promising on-field applications.