An approach is presented for designing multisensor electronic vision systems using information fusion concepts. A random process model of the multisensor scene environment provides a mathematical foundation for fusing information. A complexity metric is introduced to measure the level of difficulty associated with various vision tasks. This complexity metric provides a mathematical basis for fusing information and selecting features to minimize the complexity metric. A major result presented in the paper is a method for utilizing a priori knowledge to fuse an n-dimensional feature vector X = (X1, X2, ..., Xn) into a single feature Y while retaining the same complexity. A fusing theorem is presented that defines the class of fusing functions that retains the minimum complexity.