Abstract: In order to find out a fast nondestructive examination method for germination rate of tomato seeds, different samples of tomato seeds with six kinds of germination rates were analyzed by electronic nose, and which were classified through principal component analysis (PCA) and linear discrimination analysis (LDA). The result shows that the electronic nose could distinguish the tomato seeds with germination rate of 90%, 80%, 50%－70%and un-germination seeds. However, samples with germination rate of 50%－70% were overlapped. Based on PCA and LDA, BP neural network (BPNN) and support vector machine (SVM) were introduced in the classification. The results showed that the recognition rates for germination rate of tomato seeds by the two methods reached to 93.6% and 97.4% respectively with training set, and 65.2% and 72.7% respectively with forecast set. Compared to BPNN, SVM method has less predicting errors, which has better forecasting performance.