Aiming at the image recognition of wear particles, such as sever sliding, fatigue spall, and laminar particles, image shape feature extraction method based on shape signature and bispectrum analysis was put forward. Firstly, according to four shape signature methods which are centroid distance function, cumulative angular function, farthest point distance, and triangle area representation, the two-dimensional wear particle images were converted to one-dimensional signal. Secondly, normalized bispectrum was got by carrying out bispectrum analysis on one-dimensional signal. At last, by calculating bispectral invariants according to bispectral integration and bispectral moment on normalized bispectral domain, 76-dimensional shape feature was got, which covered whole feature, angle change information, angular point information, and contour detail information of the shape. In order to evaluate the method, shape recognition ability experiment and anti-noise ability experiment were carried on MPEG-7 CE Shape-1 Part B dataset and Swedish leaf dataset. The experiment results demonstrates that the proposed method can enhance the recognition accuracy rate and anti-noise ability of bispectrum analysis.