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An electronic nose for classification of olive oil samples is presented. Principal component analysis and a modified fuzzy artmap neural network where applied to data acquired from 12 sensors. A custom designed variable selection technique was also used to boost performance. Ten different samples of olive oils were classified with 78% accuracy, and confusion occurred mostly between similar olive oils. Defective samples were separated from defect-free olive oil with 97% accuracy. These results show that careful variable selection, coupled to a modified fuzzy artmap algorithm, can significantly improve electronic nose performance.