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Three polymers were chosen for a surface acoustic wave (SAW) sensor array and a pattern recognition approach. Based on a data set collected from 10 candidate sensitive materials for 12 analytes, the selection was accomplished by principal component analysis (PCA) and cluster analysis(CA) methods. Then, the chosen polymers were deposited on three SAW devices combined into an array, and PCA was adapted to classify three gases with similar structures. The results showed that methanol, ethanol and isopropanol could be clearly distinguished by the SAW sensor array, indicating high selectivity of the sensor array.