Significant interrelationships between skirt shapes and properties of fabrics have been found in skirt design. In this paper, the determination method, classified fabrics based on Euclidean distance and optimum cluster can be implemented by multivariate ANOVA was proposed, after testing tensile, shear and bending properties of 30 different kinds of silken fabrics, selected as subjects, with KES. Fisher discrimination module was developed by using Visual Basic 6.0 language based on Fisher discrimination functions through SPSS 10.0. Probability neural network (PNN), established based on mechanical properties of train samples, was employed to study the classification of new sample. And the classification results were studied comparatively. The results showed that the proposed method, based on Euclidean distance and multivariate ANOVA, is feasible, and those silken fabrics can be classified into three clusters. The results also indicated that Fisher discrimination module and PNN is feasible to distinguish cluster of new sample. Discrimination of silken fabrics is easy to operate because of Fisher module, and has strong robust property in noises of test samples for the reason of PNN.