Several issues in neural networks are investigated in the context of hand-written symbol recognition and a new hierarchical network structure is proposed. There have been many successful demonstrations of hand-printed character or cursive word recognition using neural network algorithms with a limited data set. However, the required network size increases as the variations allowed in the training and test patterns increase. Two highly stylized data sets (American Bankers Association's E-13B font and ANSI standard OCR-A font) are compared with hand-printed block characters in terms of required network size, mean squared error per pattern at fixed training sweeps, and number of sweeps required to reach a certain confidence level of recognition. At each training stage, the connection strengths are stored and these stored connection strengths are loaded into the net later to test generalization against similar test patterns. When the test data is presented to the net, the performance is not very good, but the network adapts to the new data quite quickly when the training and test data are very regular and/or the number of different classes for which the network was trained is small. To enhance the generalization and adaptation performance, a new hierarchical structure is proposed where the training patterns are grouped into cascade of three class problems. This structure is verified by experiments.