The problem of predicting erosion level of an axial compressor stage based on its operating parameters was solved using the machine learning methods. Current state of erosion research using the machine learning methods was reviewed, and the approach to solving the problem was proposed. Program code was developed in the Python 3 language to study the models and features applicability. The stage operation parameters obtained as a result of the numerical experiment were used as the initial data. Five erosion degrees were studied, they were determined by changing the flow pass geometry in accordance with the known laws of the erosion wear distribution. Principles are proposed for formation of the parameter-features sets explaining the erosion degree. To determine the erosion degree, simple machine learning models were used to solve the classification problems presented in the Scikit-learn Python library. Best accuracy of the study results when using pressure and temperature ratios, as well as mass flow through the axial compressor, was 0.82 (maximum one unit). When using the parameters that were actually measured in real operation, the accuracy dropped to 0.76. The random forest model showed the best results. Study results could be introduced in design and development of the compressor diagnostic systems.