Decision trees have been applied to solve a wide range of pattern recognition problems. In a tree classifier, a sequence of decision rules are used to assign an unknown sample to a pattern class. The main advantage of a decision tree over a single stage classifier is that the complex global decision making process can be divided into a number of simpler and local decisions at different levels of the tree. At each stage of the decision process, the feature subset best suited for that classification task can be selected. It can be shown that this approach provides better results than the use of the best feature subset for a single decision classifier. In addition, in large set problems where the number of classes is very large, the tree classifier can make a global decision much more quickly than the single stage classifier. However, a major weak point of a tree classifier is its error accumulation effect when the number of classes is very large. To overcome this difficulty, a fuzzy tree classifier with the following characteristics is implemented: (1) fuzzy logic search is used to find all `possible correct classes,' and some similarity measures are used to determine the `most probable class;' (2) global training is applied to generate extended terminals in order to enhance the recognition rate; (3) both the training and search algorithms have been given a lot of flexibility, to provide tradeoffs between error and rejection rates, and between the recognition rate and speed. Experimental results for the recognition of 520 most frequently used noisy Hangul character categories revealed a very high recognition rate of 99.8 percent and very high speed of 100 samples/sec, when the program was written in C and run on general purpose SUN4 SPARCstation 2.