The only independent variable in electronic computing is time. Optical computing on the other hand, has inherently two degrees of freedom, the two variables that define a point in a plane. Optical systems always process information in parallel. Such a simple optical element as a lens is capable of performing such a difficult task as a Fourier transform. Therefore, when it comes to pattern classification, optical computing is an attractive option. The Phase Only Filter has been showed to be a powerful tool for tracking objects in a two-dimensional plane. In this research, a special tracking technique is developed to overcome weaknesses of the POF under noisy circumstances. The POF is generally implemented in the 2-D plane. However, the POF has neither been trained as a pattern classifier for one-dimensional data, nor as a multi-dimensional data classifier. Methods are developed in this work to apply the POF to multi-dimensional pattern classification. Moreover, POF equivalent neural network techniques are devised and implemented for pattern classification. Two level neural network is developed for the case of multi-class classification, and a method of training is developed.