We propose a new partial shape recognition algorithm by sub-matrix matching using a proximity-based shape representation. Given one or more example object templates and a number of candidate object regions in an image, points with local maximum curvature along contours of each are chosen as feature points to compute distance matrices for each candidate object region and example template(s). A sub-matrix matching algorithm is then proposed to determine correspondences for evaluation of partial similarity between an example template and a candidate object region. The method is translation, rotation, scale and reflection invariant. Applications of the proposed partial matching technique include recognition of partially occluded objects in images as well as significant acceleration of recognition/matching of full (non-occluded) objects for object based image labeling by learning from examples. The speed up in the latter application comes from the fact that we can now search only those combinations of regions in the neighborhood of potential partial matches as soon as they are identified, as opposed to all combinations of regions as was done in our prior work [Xu et al., Object formation and retrieval using a learning-based hierarchical content-description, Proceedings of the ICIP, Kobe, Japan 1999]. Experimental results are provided to demonstrate both applications.