An electronic tongue system based on virtual instrument technology was developed and used todistinguish andanalyze 4 kinds of Chinese patent medicines in the treatment of cold symptom. The respond signal of electronic tongue was first preprocessed by the feature point extraction (FPE)method and discrete wavelet transform (DWT)method, respectively. According to clustered property and classification results of sample points, the DWT applied “db4”as mother wavelet and decomposed 8 levels was selected as a recommended feature extraction method.The principal component analysis(PCA),cluster analysis (CA)and back propagation neural network (BPNN) were then used to distinguish and identify the 4 kinds of Chinese patent medicines.The results showed that the cumulative contribution rate of PC1 and PC2 was reached 95.6% when PCA was employed.All medicines were effectively distinguished except that Lingyang cold tablet and Yinqiao antidotal tablet had an overlapping trend. The CA could obviously observe the dissimilarity of the 4 kinds of Chinese patent medicines, but the classification result of CA was so poor that 4 kinds of medicines were classified into 2 groups, while nonlinear model BPNNexhibited a better result than other classification model. The parameters of BPNN such as the training algorithm, the activation function and the number of hidden layer nodes were optimized and determined for improving the model performance. The validation set results indicate that all samples are perfectly discriminated by BPNN with the correct classification rate reaching 100%. This research can provide a technical reference for the research on nonsensory quality evaluation and identification of Chinese patent medicines.