The majority of automatic target recognition (ATR) studies are formulated as a traditional classification problem. Specifically, using a training set of target exemplars, a classifier is developed for application to isolated measurements of targets. Performance is assessed using a test set of target exemplars. Unfortunately, this is a simplification of the ATR problem. Often, the operating conditions differ from those prevailing at the time of training data collection, which can have severe effects on the obtained performance. It is therefore becoming increasingly recognised that development of robust ATR systems requires more than just consideration of the traditional classification problem. In particular, one should make use of any extra information or data that is available. The example in this paper focuses on a hybrid ATR system being designed to utilise both measurements from identity sensors (such as radar profiles) and motion information from tracking sensors to classify targets. The first-stage of the system uses mixture-model classifiers to classify targets into generic classes based upon data from (long range) tracking sensors. Where the generic classes are related to platform types (e.g. fast-jets, heavy bombers and commercial aircraft), the initial classifications can be used to assist the military commander's early decision making. The second-stage of the system uses measurements from (closer-range) identity sensors to classify the targets into individual target types, while taking into account the (uncertain) outputs from the first-stage. A Bayesian classifier is proposed for the second-stage, so that the first-stage outputs can be incorporated into the second-stage prior class probabilities.