In this study, artificial neural networks are proposed to predict the concentrations of SO₂ and PM at two different stations in Zonguldak city, a major coastal mining area in Turkey. The established artificial neural network models involve meteorological parameters and historical data on observed SO₂, PM as input variables. The models are based on a three-layer neural network trained by a back-propagation algorithm. The models accurately measure the trend of SO₂ and PM concentrations. The results obtained through the proposed models show that artificial neural networks can efficiently be used in the analysis and prediction of air quality.