Three gases are tested to investigate the effects of data preprocessing algorithm and optimization of sensor array on electronic noses. Preprocessing algorithms are chosen via principal component analysis (PCA), and the relative difference algorithm is determined for preprocessing data of the el ectronic nose for its good classification effect. To optimize the initial array, we first remove sensors abnormally responsing by observing the sensors′ res ponse trend and coefficient of variation. Then we analyze PCA factor loading and conduct multi-collinearity test to determine possible optimal array s using the correlation coefficient and variance inflation factor analysis. Final ly, we apply back propagation(BP) neural network to verify the possible optimal arrays through g as recognition. We determine the final array as well as select other array for controlled study. The results of the check computation certify th at the optimization method of sensor array can not only e liminate anomalies and redundant sensors, but also works well on the classificat ion of test samples.