Abstract: The value of E-nose response signals differed with different levels mechanical (0 pricks, 30 pricks, 60 pricks and 90 pricks) damaged tomato plants, indicating that the emission of volatiles by tomato plants changes in response to different degrees of damage. The tomato plants with different levels mechanical damages were classified through principal component analysis (PCA) and linear discrimination analysis (LDA). The result showed that the electronic nose could distinguish different damaged tomato plant by LDA. However, samples by PCA were overlapped. Stepwise discriminant analysis (SDA) and back-propagation neural network (BPNN) were applied to evaluate the data. The average correction ratios of testing set of SDA and BPNN were 84.4% and 93.8% respectively. The results indicate that it is possible to classify different degrees of damaged tomato plants using e-nose signals.