Abstarct: Today, In this thesis, the problem of image retrieval for fighter aircrafts from a dataset consists of 10 different fighter classes is studied. In this project, a CBIR system is developed which found similar aircrafts to a query aircraft picture from databaxse. The difference of viewpoint in query picture and search domain pictures is the main challenging problem in image retrieval systems. We tackle this problem using 3D models of different fighter aircrafts classes. We use the concept of virtual cameras around a surrounding spherical surface, in order to prepare a lot of reference pictures of the aircrafts from different viewpoints. We propose three different methods for feature extraction and similarity evaluation: 1) cross correlation of aircrafts silhouette, 2) histogram of oriented gradients, and 3) Zernike moments, which the latter is robust to rotation. Simulation results show that the best retrieval accuracy (about 80.8%) is achieved by using Zernike moments method. Regarding that, there are some similar appearance classes of fighter aircraft in databaxse the performance of the developed CBIR system seems sufficiently good.