Recognition of targets in flir imagery has been a goal of military weapon systems since the initial development of flir sensors. Reliable systems to automatically recognize targets in flir imagery have thus far eluded the combined efforts of the DOD services. Historical approaches have concentrated on adaptation of pattern recognition techniques from visible imagery (TV) target recognition. Recent research has suggested that consideration of target characteristics unique to IR imaging such as self emission due to thermal mass may lead to improved recognition performance. In order to effectively utilize these characteristics, predictive models are needed to establish the combination of viewing conditions and target states for which the target's thermal characteristics manifest themselves. This paper will focus upon the use of signature prediction models as a component of a recognition algorithm in the context of model-based vision (MBV).