Differentiation of syndromes is the kernel theory of Traditional Chinese Medicine (TCM). How to diagnose syndromes correctly with scientific means according to symptoms is the first problem in TCM. Several modern approaches have been applied, but no satisfied results have been obtained because of the complexity of diagnosis procedure. Support Vector Machine (SVM) is a new classification technique and has drawn much attention on this topic in recent years. In this paper, we combine non-linear Principle Component Analysis (PCA) neural network with multi-class SVM to realize differentiation of syndromes. Non-linear PCA is used to preprocess clinical data to save computational cost and reduce noise. The multi-class SVM takes the non-linear principle components as its inputs and determines a corresponding syndrome. Analyzing of a TCM example shows its effectiveness.