Existing computer simulations of aircraft infrared signature do not account for the dispersion induced by uncertainty on input data, such as aircraft aspect angles and meteorological conditions. As a result, they are of little use to estimate the detection performance of IR optronic systems: in that case, the scenario encompasses a lot of possible situations that must indeed be addressed, but can not be singly simulated. In this paper, we focus on low resolution infrared sensors and we propose a methodological approach for performing a classification of different aircraft on the resulting set of low resolution infrared images. It is based on a maximum likelihood classification which takes advantage of Bayesian dense deformable template models estimation. This method is illustrated in a typical scenario, over a database of 30 000 simulated aircraft images. Assuming a white noise background model, classification performances are very promising, and appear to be more noise-robust than support vector machines ones