Morphological transformation provides a powerful, nonlinear means of quantitatively analyzing data sets such as images. This technique has traditionally been applied to feature location or feature removal, as in noise removal. However, the technique holds some promise for fast object classification. By viewing the transformation as a neural network, proven training techniques may be applied to optimize the performance. The critical step in applying morphology is the design of the structuring element or shape of the filter. By casting the problem as that of object classification and by properly defining error functions, neural network training techniques may be used to optimize performance. In addition, this view of the procedure as a neural network allows the generalization of the technique to include sequences of filters, which correspond to multiple layer neural networks. an optical architecture is being considered to implement a sequence of morphological transformations, taking into account known principles and limitations of the optics and of neural networks, in order to perform a complex object classification task. Then the corresponding morphological filter parameter will be optimized using neural network training techniques.