We describe a new, publicly accessible Chinese character recognition system based on a nearest neighbor classifier that uses several sophisticated techniques to improve its performance. To increase throughput, a 400-dimensional feature space is compressed through multiple discriminant analysis techniques to 100 dimensions. Recognition accuracy is improved by scaling these dimensions to achieve uniform variance. Two neural network classifiers are compared using the new feature space, Kohonen's Learning Vector Quantization and Geva and Sitte's Decision Surface Mapping. Experiments with a 37,000 character ground truthed dataset show performance comparable to other systems in the literature. We are now employing noise and distortion models to quantify the robustness of the recognizer on realistic page images.