A gas recognition system was fabricated using a conducting polymer (polypyrrole and polyaniline) sensor array that can recognize and analyze various kinds and quantities of volatile organic compounds (VOCs), such as ethanol, toluene, benzene, and chloroform. The sensors also exhibited different sensitivity curves to VOCs according to the different additive amounts and kinds of conducting polymer (polypyrrole or polyaniline) and electrode. Polypyrrole and polyaniline film sensors made by chemical polymerization were employed to detect the VOCs. The multi-dimensional sensor signals obtained from the sensor array were then analyzed using the principal component analysis (PCA) technique and a radial basis function network (RBFN). By implementing the sensing signals from the sensor array along with a multi-layer neural network using an Radial-Basis Function learning algorithm, the sensor array was successful in accurately classifying the gas species and also identifying the concentration of each VOC in a real-time process.