In this paper, we propose a new approach for 3D-shape matching. This approach encloses an off-line step and an on-line step. In the off-line one, an alphabet, of which any shape can be composed, is constructed. First, 3D-objects are subdivided into a set of 3D-parts. The subdivision consists to extract from each object a set of feature points with associated curves. Then the whole set of 3D-parts is clustered into different classes from a semantic point of view. After that, each class is modeled by a Hidden Markov Model (HMM). The HMM, which represents a character in the alphabet, is trained using the set of curves corresponding to the class parts. Hence, any 3D-object can be represented by a set of characters. The on-line step consists to compare the set of characters representing the 3D-object query and that of each object in the given dataset. The experimental results obtained on the TOSCA dataset show that the system efficiently performs in retrieving similar 3D-models.