This paper proposes a simple yet novel method for recognition of certain sorts of moving entities incorporating their shape and motion patterns. Although shape features have been commonly employed in object recognition, motion characteristics are in general not integrated to geometric models. In the interest of utilizing the motion attributes, the trajectories are investigated to extract the 'coherence quality' of the entities. Besides, at every step a geometric shape model is adopted and the parameters defining the shape model are utilized in obtaining the prior probabilities of the entities being a member of a particular class of interest. The coherence quality is used to get the posterior probabilities through a Bayesian approach. The main contribution of this paper is the incorporation of coherence quality in identification of moving entities. The proposed method is tested against clutter and occlusion in an uncontrolled environment with patterns collected from over 500 entities. It is shown to yield a satisfactory performance rate of 92% over the entire dataset with significant generalization capabilities without any restrictions on the application setting and with considerable occlusion and clutter.