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.