We present a two-stage process for target identification and pose estimation. A database of possible target states, i.e. identity and pose, is precomputed by a two-step clustering procedure, reflecting the two stages of the identification process. The current database is based on images generated from 3D CAD models of military ground vehicles on which realistic infrared textures have been applied. At the coarse level, the database is divided into a set of clusters, each represented by a small set of eigenimages, obtained through principal component analysis (PCA). The classification at this level is achieved by measuring the orthogonal distance between the region of interest (ROI) and the eigenspace of each cluster. Each cluster itself contains a few subclusters. A support vector machine is employed for a pairwise discrimination of subclusters. The likelihood that the target belongs to a particular cluster/subcluster is based on histograms, obtained at the time of training of the system. In addition to the classification of individual images it is also possible to handle image sequences where the pose of the target might vary in subsequent image frames. In this situation, the pose is assumed to change according to a first-order Markov process. The overall probability for each target state is accumulated through recursive Bayesian estimation. The performance of the above procedure has been evaluated through the identification of targets in synthetic image sequences, where the targets are placed in realistic backgrounds. Currently , we are able to correctly identify the targets in more than 80 percent of the image sequences. In about 60 (80) percent of the cases the pose can be estimated within an accuracy of 10 (20) degrees. The accuracy of the pose estimation is limited by the size of the sub-clusters.