Abstract: The electronic nose was used to measure the volatile compounds of different vinegars, including Eastlake vinegar, Shuita rice vinegar, Haitian rice vinegar, Hengshun aromatic vinegar, Shuita mature vinegar and Zilin mature vinegar.The radial basis function ( RBF) neural network and Fisher discriminant were applied to predict different brands of vinegar. The results showed that the response values of sensors S7 and S3 changed significantly ( p < 0.05) among different samples.That meant the contents of ammonia and hydrogen sulfide had significant differences among six kinds of vinegar.Moreover, linear discriminant analysis could better distinguish different brands of vinegar than principal component analysis. Both Fisher discriminant and RBF were able to predict different brands of vinegar.In contrast, Fisher discriminant had more accurate prediction. The correct discriminant rate of Fisher model was 100%. Therefore, the electronic nose could identify the brands of vinegar, maintain the brand reputation of vinegar and regulate the market.