The purpose of the research is to develop a method for synthesizing models for assessing the state of RAM of operators of human-machine systems, the use of which in the decisive rules for predicting and diagnosing the states of RAM and its blocks ensures an increase in the quality of decisions made.Methods. To monitor the state of various RAM blocks, the following set of techniques was selected: searching for a signal in noise; "identification"; full reproduction; identification of missing digits; Memory. To select an adequate mathematical research apparatus, an exploratory analysis of the structure of the processed data was carried out, during which it was found that the selected classes of RAM states are of a fuzzy nature with uncertain boundaries of their intersections. Taking into account the peculiarities of the processed data, the selected methodology was modified by developing a new method for fuzzy assessment of the state of RAM based on the characteristics of its properties in combination with informative features characterizing the ergonomics of the workplace, the environmental component and individual risk factors.Results. In the course of the research, a model was synthesized for predicting the appearance and development of dysfunctions of RAM in operators of information-rich systems, characterized by the use of indicators characterizing the state of RAM blocks as predictors, which allows one to obtain confidence in the correct decision-making of no worse than 0,85.Conclusion. In the course of the studies, it was shown that in order to improve the quality indicators of forecasting and diagnosing the states of RAM and its blocks, when synthesizing the corresponding decision rules, indicators characterizing the state of RAM blocks, energy imbalance of BAP, ergonomic and individual risk factors should be taken into account. With this approach, in forecasting problems, confidence in the correct decision-making is achieved at least 0,85. In the tasks of diagnosing the early stages of RAM disorders among operators of information-rich systems, confidence in correct decision-making exceeds 0,95.