90%。 An investigation was made to determine the four tea samples with different quality grade by using an electronic nose (e-nose). The response signals of e-nose were analyzed under different sampling conditions by variance analysis and multivariance analysis. Analytical results showed that the different volume of vials and the different collection times have significant effect on the response signals of the e-nose. Then the data were processed using principal components analysis (PCA), linear discrimination analysis (LDA) and artificial neural network (ANN). The results analyzed by LDA were superior to that by PCA, which could distinguish all the tea samples completely. However, PCA method could not estimate sample of A280 correctly. Further 90% correct classification was achieved for all the tea samples using the BP neural network.