In this paper, an electronic nose data classification approach based on least square support vector machines (LS-SVM) in combination with principal component analysis (PCA) is investigated. The electronic nose data are first converted into PCA, where the data are projected from a high dimensional space into a low dimensional space, preferably two or three dimensions. Then the resulting features from the PCA are sent into the LS-SVM classifier in order to recognize the gas category. The performance of the proposed approach is validated by cross-validation technique. An experiment has been demonstrated by using coffee data from different types of coffee blends. Experimental results show that the LS-SVM in combination with PCA is an effective technique for the classification of electronic nose data.