This　paper　presents　a　new　identification　method　to　identify　the　main　errors　of　the　machine　tool　in　time-frequency　domain.　The　low-and　high-frequency　signals　of　the　workpiece　surface　are　decomposed　based　on　the　Daubechies　wavelet　transform.　With　power　spectral　density　analysis,　the　main　features　of　the　high-frequency　signal　corresponding　to　the　imbalance　of　the　spindle　system　are　extracted　from　the　surface　topography　of　the　workpiece　in　the　frequency　domain.　With　the　cross-correlation　analysis　method,　the　relationship　between　the　guideway　error　of　the　machine　tool　and　the　low-frequency　signal　of　the　surface　topography　is　calculated　in　the　time　domain.