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Sensors practitioners don't make full use of the power of state-of-the-art pattern recognition (PR) algorithms and software. In this paper we apply -to our knowledge for the first time-Random Forests (RF) and Nearest Shrunken Centroids (NSC) to the classification of three E-Nose datasets of different hardness. We compare the classification rate with the one obtained by SVM. The classifiers parameters are optimized in an inner cross-validation (CV) cycle and the error is calculated by outer CV in order to avoid any bias. RF and SVM have a similar classification performance (SVM has an edge on the most difficult dataset). On the other hand, RF and NSC have an in-built feature selection mechanism that is very helpful for understanding the structure of the dataset and evaluating sensors.