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An automatic recognition system that can recognize objects of interest no matter where it is being used or in what scenario it is being deployed is of immense value in numerous applications areas such as robotic , automatic target recognition(ATR), reconnaissance , and remote sensing. The difficulty in building such a system lies in the decade long observation that candidate automatic object recognition (AOR)systems perform well when they are used in domains for which they have been initially trained for . When the environmental conditions , scene content or scenario changes the behavior of such systems become very erratic and unpredictable. In this paper we describe a technique for the automatic adaptation of the multi sensor automatic object recognition systems.The adaptation covers both selection of optimum sensor frequency bands and the AOR's internal algorithmic parameters. The adaptation is done by creating empirical models of the AOR's performance measures as functions of data metrics , internal algorithms' parameters, sensors' wavelengths, types and sensor combinations. Then the optimum values of the internal parameters and the optimum sensor frequencies will be computed by optimizing the performance models as new signal metrics are obtained. These metrics vary due to the outside changes in the scene , scenario , or environmental conditions. The technique is applicable to a wide range of automatic recognition systems. It provides for the first time an integrated approach for simultaneous and automatic algorithm parameter and sensor wavelength adaptation
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