A value function structure is proposed to solve the problem of multiple sensor integration. The value of a sensor or a group of sensors will be a function of the number of possible object contenders under consideration and the number that can be rejected by using the information available. It will also depend on the current state of the environment and can be redefined to indicate changes in sampling frequency and/or resolution for the sensors. A theorem prover will be applied to the sensor information available to reject any contenders. The rules used by the theorem prover can be different for each sensor while the integration is provided by the common decision space. This overcomes any incompatibility between different sensors with respect to feature extraction and pattern recognition algorithms. A database will be used to store the values for different sensor groups and the best search paths, and can be adaptively updated, thus providing a training methodology for the implementation of this approach.