A relational template matching ATR algorithm, a specialization of a more general algorithm design approach under the name of information-based complexity, has recently demonstrated promising results for simulated terrain board imagery. The merit of this approach is a design principle exemplified as follows. The estimation of auxiliary unknowns, e.g., boundary estimation, are not made prematurely on partial information such as low level image structure. Instead, the algorithm process considers an appropriate set of provisional boundaries which are economically represented by taking advantage of any structure present in this set. At each stage, the relative uncertainty of each boundary in the set is expressed by a probability. This uncertainty is refined by updating these probabilities by an intelligent choice of additional relevant information consisting of data and target-and-clutter model structure. This process is such that the optimal information about the auxiliary unknowns such as boundary is obtained simultaneously with the classification decision of the image region (e.g., ID). Thus, the design principle governs the intelligent choice of new information based on previous processing results, and economically represents any auxiliary information or unknowns by learning their hidden structure in the preprocessing or solution development stage. The objective of this paper is to describe the natural extension of this paradigm to multisensor imagery.