In this paper we propose a novel algorithm for 3D shape searching based on the visual similarity by cutting the object into sections. This method rectify some of the shortcomings of the visual similarity based methods, so that it can better account for concave areas of an object and parts of the object not visible because of occlusion. As the first step, silhouettes of the 3D object are generated by partitioning the object into number of parts with cutting planes perpendicular to the view direction. Then Zernike moments are applied on the silhouettes to generate shape descriptors. The distance measure is based on minimizing the distance among all the combinations of shape descriptors and then these distances are used for similarity based searching. We have performed experiments on the Princeton shape benchmark and the Purdue CAD/CAM database, and have achieved results comparable to some of the best algorithms in the 3D shape searching literature.