This study deals with the shape recognition problem using the Hidden Markov Model (HMM). In many pattern recognition applications, selection of the size and topology of the HMM is mostly done by heuristics or using trial and error methods. It is well known that as the number of states and the non-zero state transition increases, the complexity of the HMM training and recognition algorithms increases exponentially. Oil the other hand, many Studies indicate that increasing the size and non-zero state transition does not always yield better recognition rate. Therefore, designing the HMM topology and estimating the number of states for a specific problem is still all unsolved problem and requires initial investigation on the test data.