Optical pattern recognition is significantly improved by exploiting the attributes of both optics and electronics in architectures containing combinations of optical systems and electronic processors. Essential interfaces for such hybrid systems are spatial light modulators and optoelectronic recording devices. These devices suffer from technological limitations that must be alleviated by suitably designed optical architectures and efficient processing algorithms. We review several correlator architectures and discuss their adaptability to hybrid systems. Due to the computational complexity encountered in such hybrid electrooptical systems, iterative optimization methods can be efficiently employed. Some of the algorithms presented are especially useful for treating real physical systems having properties that cannot be exactly defined or quantified. Algorithms that were successful in these architectures belong to two families, the basically stochastic algorithms and the projection-onto-constraint-sets algorithms. The former is particularly suitable for applications in hybrid electro-optical learning systems, while the latter is extremely powerful for the design of spatial filters with properties that are difficult to achieve by other means. A case study is given of an adaptive correlator for rotation-, scale-, and shift-invariant pattern recognition.