This article describes the application of a cellular neural network (CNN) to model air pollutants. In this study, forthcoming daily and hourly values of particulate matter (PM10) and sulphur dioxide (SO2) were predicted. These air pollutant concentrations were measured at four different locations (Yenibosna, Sarachane, Umraniye and Kadikoy) in Istanbul between 2002 and 2003. Eight different meteorological parameters (temperature, wind speed and direction, humidity, pressure, sunshine, cloudiness, rainfall) recorded at Florya and Goztepe meteorological stations were used to model inputs. First, the results of CNN prediction and statistical persistence method (PER) were compared. Then, CNN and PER outputs were correlated with real time values by using statistical performance indices. The indices of agreement (d) for daily mean concentrations were found using CNN and PER prediction models: 0.71-0.80 and 0.71-0.78 for PM10, and 0.81-0.84 and 0.77-0.82 for SO2 in all air quality measurement stations, respectively. From these values, CNN prediction model are concluded to be more accurate than PER, which is used for comparison. In hourly prediction of mean concentrations with CNN, d value is found as 0.78 and 0.92 for PM10 and SO2, respectively. Thus, it was concluded that CNN-based approaches could be promising for air pollutant prediction.