As traditional object matching algorithms can not match precisely a partially occuluded object, this paper proposes a novel partially occluded object matching algorithm based on the description and evaluation of contour features. Firstly, the contour fragments to describe the local object feature are obtained by splitting the object contour with contour curvature,and those contour fragments are merged and classified according to the object skeleton to describe the object features at multi-levels. Then, two evaluation parameters, importance and partiality, are defined for those contour fragments. The former evaluates the importance of the local feature, and the latter evaluates the proportion of contour fragment to the whole contour. Finally, the two evaluation parameters of contour fragment and the similarity between contour fragments are derived to obtain the weighted partial similarity to measure the matching degree of the partially occluded object reasonably and to obtain the optimal matching result. Compared with some current matching algorithms, the proposed algorithm improves the average recognition rate about 1.5% under various occluded cases.