THE PURPUSE. To identify the main problems of automated monitoring results interpretation associated with instrument transformers’ insulation state assessment. To formulate recommendations and possible solutions to solve the described problem. METHODS. Mathematical modeling of physical processes and general approaches of system analysis, as well as methodological provisions for systemic studies in the energy sector were used. RESULTS. Within the framework of the study, the possibility of using partial discharge monitoring systems was analyzed together with its implementation effectiveness analysis for oil-filled current and voltage transformers at a real power network facility. Also, within the framework of the presented work, modern methods of partial discharges measurement in the high-voltage power equipment insulation were analyzed and the application of the electric method for identifying partial discharges in the insulation of oil-filled high-voltage instrument transformers was substantiated. CONCLUSION. Modern automated systems for technical state monitoring are most often developed for a specific type of power equipment and their application for other types of equipment requires significant improvement not so much from a technical point of view, but from the point of view of defect database development. High effectiveness of monitoring systems application can be achieved by transforming such systems into self-learning intelligent systems. Self-learning in this case does not mean explicit autonomy, but the possibility of self-adjustment of the system by means of self-modifying rules. This approach makes it possible to unify not only the mathematical models themselves, since there is no need to develop individual models for each type of power equipment, taking into account the peculiarities of design and construction, but also to optimize the application of such models in the information network of the enterprise, improving themselves through the accumulated experience.