The subject of the research is methods and means of identifying the state of a computer system . The purpose of the article is to improve the quality of computer system state identification by developing a method based on ensemble classifiers. Task: to investigate methods for constructing bagging classifiers based on decision trees, to configure them and develop a method for identifying the state of the computer system. Methods used: artificial intelligence methods, machine learning, ensemble methods. The following results were obtained: the use of bagging classifiers based on meta-algorithms were investigated: Pasting Ensemble, Bootstrap Ensemble, Random Subspace Ensemble, Random Patches Ensemble and Random Forest methods and their accuracy were assessed to identify the state of the computer system. The research of tuning parameters of individual decision trees was carried out and their optimal values were found, including: the maximum number of features used in the construction of the tree; the minimum number of branches when building a tree; minimum number of leaves and maximum tree depth. The optimal number of trees in the ensemble has been determined. A method for identifying the state of the computer system is proposed, which differs from the known ones by the choice of the classification meta-algorithm and the selection of the optimal parameters for its adjustment. An assessment of the accuracy of the developed method for identifying the state of a computer system is carried out. The developed method is implemented in software and investigated when solving the problem of identifying the abnormal state of the computer system functioning. Conclusions. The scientific novelty of the results obtained lies in the development of a method for identifying the state of the computer system by choosing a meta-algorithm for classification and determining the optimal parameters for its configuration.