We present a novel method for 3D-shape matching using Bag-of-Feature techniques (BoF). The method starts by selecting and then describing a set of points from the 3D-object. Such descriptors have the advantage of being invariant to different transformations that a shape can undergo. Based on vector quantization, we cluster those descriptors to form a shape vocabulary. Then, each point selected in the object is associated to a cluster (word) in that vocabulary. Finally, a BoF histogram counting the occurrences of every word is computed. These results clearly demonstrate that the method is robust to non-rigid and deformable shapes, in which the class of transformations may be very wide due to the capability of such shapes to bend and assume different forms.