Gas chromatography-mass spectrometry (GC-MS) is widely used in many fields because of its high sensitivity, high resolution and reproducibility. The major challenge of this analytical technology is the identification of components in complex samples. Generally, mass spectral library searching is commonly employed to assist in the identification of unknown spectra. However, this widely available method just provides a hit-list of candidates ordered by their numerical similarity indices. When an unknown compound has many isomeric compounds or is absent from the reference library, this approach might be less useful. Classification of mass spectra, a complementary technique to the library searching, is beneficial to computer-aided mass spectral interpretation but suffers from the fact that the variables used in the classifier are usually uninterpretable. In this study, a novel classifier is built based on data mining and feature analysis. In this classifier, the neutral loss is skillfully used to identify the differences between mass spectra of alcohols and ethers in the data set. After comparison with two chemometric methods, Fisher ratios linear discriminant analysis (LDA) and genetic algorithm partial least squares discriminant (GA-DPLS) analysis, it is found that our method achieves a better predictive ability. More importantly, this method is able to predict whether compounds could be classified correctly or not.