In the present paper, an efficient method for three dimensional aircraft pattern recognition is introduced. In this method, a set of simple area based features extracted from silhouette of aerial vehicles are used to recognize an aircraft type from its optical or infrared images taken by a CCD camera or a FLIR sensor. These images can be taken from any direction and distance relative to the flying aircraft. A multilayer perceptron neural network has been used for the purpose of aircraft classification. The network training has been carried out using a library of images generated by a 3D model of each aircraft. The neural network is successfully trained and used to recognize and classify arbitrary real aircraft images. The results show more than 90% accuracy in ideal conditions and very good robustness in the presence of noise.