A novel SO₂ electrode made from polyaniline/Nafion membrane was developed. The electrode was designed to detect SO₂ content at ppm levels from the exhaust gas of factories. An SO₂ concentration of 20–250 ppm was measured through a response current and a neural network was introduced to learn the response signal and predict the SO₂ concentration. By providing the whole pattern of the response currents at different sampling times as input nodes of the network, the corresponding SO₂ concentration can be learned and predicted as the output of the network. Such a parallel approach is different from the conventional calibration method. The latter takes 6 min to reach a steady response. Neural network signal pattern detection does not necessarily require a steady response, and as a result, the response time could be reduced to 3 min. The amplified signal in the conventional calibration method often requires a noise filter which is not necessary for the neural network approach because of its fault tolerance ability. A saturation-type transfer function bx/(1+x) was chosen and excellent results were obtained. Response currents acquired at different sampling intervals corresponding to different numbers of input nodes, were compared by examining the training and prediction ability of the neural network. The minimum number of training data for accurate predictions was also determined.