The fusion of information from different sensors can provide features that cannot be obtained from either of the sensors. In this paper we examine a feature that is obtained from the fusion of information sensed from thermal (long wave infrared) and visual imagery. Robust object recognition requires object features that are invariant to scene conditions and viewpoint. Previous effort in developing such features using explicit constraints of invariance has mostly considered only visual data. The investigation of the variation of quasi-invariant features for different ranges of viewing/scene parameters has also been limited to geometric features derived from visual imagery. Such investigation has not been performed for features derived from multisensory imagery or from thermal (infrared) imagery. In this paper we conduct a detailed analysis of the variation of invariant and quasi-invariant features that have been proposed for the analysis of thermal (infrared) imagery, and also for the integrated analysis of thermal and visual imagery. The features are based on thermophysical models of energy exchange between the object and the scene. Examination of feature variation is based on simulating the various scene and object energy components for varying scene parameter values and predicting the feature value. This approach eliminates the expensive and impractical task of collecting real imagery under all possible scene conditions. We develop thermophysical object models based on equivalent thermal circuits. The models use a small number of nodes to predict the surface temperatures (thermal image gray levels) and to predict the feature values on the surfaces of complex objects. This approach reduces the computational cost of simulation and facilitates model construction. The sensitivity of the features to object and scene parameter variations is evaluated. The features are shown to be relatively robust within specific ranges of scene conditions.