Measurement and classification of partial discharges are an important tool for the evaluation of insulation systems used in high voltage equipments. After pre-processing of data, which captures, scans and filters the signal of partial discharges, generally eliminating noises, there are basically two main steps, which are the extraction of characteristics and the pattern classification. Partial discharges contain a set of unique discriminatory characteristics that allow them to be recognized. Thus, the first procedure in the classification process is to define which of them can be used and which is the method for extraction of those characteristics. The phenomenon of partial discharges has a transient nature and is characterized by pulsating currents with a duration of several nanoseconds up to a few microseconds. Its magnitude is not always proportional to the damage caused, and discharges of small magnitude can quickly lead to the evolution of a failure. Therefore the need to understand this phenomenon well and to know how to interpret the data. In addition, large high voltage equipments such as motors and generators may have more than one internal source of partial discharges, and it is important to separate the signals from those different sources prior to classification. In the case of smaller high voltage equipments, as surge arrester and substation current transformers, the simple detection of the presence of partial discharges inside the equipment, regardless of the number of sources, is sufficient to indicate the withdrawal of operation of the equipment, given their low relative cost and the high degree of importance of these to the reliability of the system where they are part of. For a complete and reliable diagnosis of high voltage insulations, there is a demand for an analysis system capable of effectively promoting the detection of the partial discharges internal to the equipments, the separation of the various sources of partial discharges in the case of large equipments, as well as to carry out the correct classification of the type of failure. The system should be based mainly on the analysis of the discriminating characteristics of the different sources and the signature of the signals for the different failure. This study contributes to fill this gap by presenting methodologies that are robust and accurate in the tests performed, so that they can effectively guide maintenance specialists in decision making. To do this, new variables are proposed to extract relevant information from time signals measured in various types of insulations, being applied here in field and laboratory data to evaluate their effectiveness in the task. This information is treated using standard classification techniques and artificial intelligence to automatically determine the presence of partial discharges, the number of different sources and the type of defect in the high voltage insulations used in the study. Another contribution of the study is the creation of a historical database, based on image processing, with partial discharge map patterns known in the literature on rotating machines, to be used in the classification of new maps measured in this type of equipment.