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Abstract Reliable operating conditions of hydrostatic turntables are prerequisite to ensuring machine tool performance. The hydrostatic turntable is affected by multiple working conditions, therefore, methods for evaluating turntable load-carrying capacity has become research hotspot. In this paper, by analyzing bearing capacity, parameters closely related to the support performance of turntables are selected as recognition features and an artificial neural network (ANN) training method is proposed. The ANN method is based on numerical solutions of over-determined nonlinear equations (ODNE) to intelligently evaluate turntable performance. In this study, ANN and ODNE training are applied to evaluate the performance of hydrostatic turntables. Finally, to verify the feasibility of the method, an intelligent monitoring system is established to collect data on machine tools. Highlights • Factors influencing the performance of hydrostatic turntable were determined by analyzing the carrying ability of oil pads. • Based on the ANN structure for fast calculation of optimal weights, an ODNE training method was presented. • For intelligent monitoring of a hydrostatic turntable, BP and ODNE training are both recommended for training the ANN. [ABSTRACT FROM AUTHOR]