In this paper, we present a novel occlusion resistant shape classification scheme for hidden Markov modeled shapes. First, hidden Markov model (HMM) is built using multiple example shapes for each shape class. A reference path for each class is built from the corresponding HMM, which is nothing but optimal path followed by the most likely example shape. The reference path stores temporal information about the entire shape, while the HMM only retains relationship between temporal information. Finally to classify a shape, occluded or not, its optimal path through HMM is calculated and warped to match the reference path using dynamic time warping (DTW). Correct class is identified as the one for which the warping cost is minimum. Classification results obtained for two shape data sets are presented for varying degrees of occlusion and are compared with the conventional maximum likelihood (ML) HMM classifier.