Abstract: In Computer Vision, two-dimensional shape classification is a complex and well studied topic, often basic for three-dimensional object recognition. Object contours are a widely chosen feature for representing objects, useful in many respects for classification problems. In this paper, we address the use of Hidden Markov Models (HMMs) for shape analysis, based on chain code representation of object contours. HMMs represent a widespread approach to the modeling of sequences, and are largely used for many applications, but unfortunately it is poorly considered in literature concerning shape analysis, and, in any case, without reference on noise or occlusion sensitivity. In this paper HMM approach to shape modeling is tested, probing good invariance of this method in term of noise, occlusions, and object scaling.