We develop a model-based vision algorithm to estimate the aspect angle of a target in a forward-looking infrared (FLIR) image. In the preprocessing stage of the algorithm, a set of 3-D voxel-based models is created using a CAD/CAM package. These models are rotated about the vertical axis through a series of predetermined angles and then projected onto the horizontal plane. This gives us a database library of 2-D images. We select as signature from a given FLIR image and attempt to match it with the various images in the given database library of images using the normalized cross-correlation method. The angle of rotation corresponding to the image in the database library giving the best possible match is estimated to be the aspect angle of the signature (target). We use an algebraic approach to represent images and the process involves certain algebraic operations on the polynomials. An advantage of the algebraic approach is that a high speedup in the run time is possible if the fast Fourier transform is used to compute the polynominal multiplications involved in the processing.