In this paper, we present a new model to recognize a three-dimensional (3D) object. which uses six Hu moment invariants and six Zernike moments as a feature vector. It extends our former work on 3D-object recognition based on Hu moment invariants [1]. The classification of the desired 3D object using Hu-moment model [1] is based on selecting the object that corresponding the minimum distance of the six minima obtained from six reference libraries and the desired object. The classification may be correct if we selected the object that corresponding the second minimum distance value instead of the first value. To select the correct one, this paper describes another model to recognize a 3D object based on Hu and Zernike moment invariants as a feature vector. Zernike moment invariants are used to find the pose of the aircraft at first to know from which library we can make the decision. The proposed model differs significantly from many recent 3D recognition models, which emphasize on stereo reconstruction and structured light analysis. Several trajectories for 3, 6, and 9 aircraft are generated, using 3D Studio Max software. To study the performance of the proposed model, the aircraft patterns were chosen to test the proposed model because the views of these patterns are relatively difficult due to the similarity of their images. Finally, we show that the proposed model achieves high recognition rates compared with that of the View Information Encoded With Network model (VIEWNET) [2] and the Hu moment invariants [1] using the approximately the same set of objects and using the same decision rule.