Air quality has been deteriorated seriously in urban areas as a result of increasing anthropogenic activities. Meteorological conditions affect air pollution levels in the urban atmosphere significantly due to their important role in transport and dilution of the pollutants. This paper aims to investigate usability of some promising statistical methods for examining the impacts of metrological factors on SO2 and PM10 levels. Data were collected from city centre of Kocaeli in winter periods from 2007 to 2010 as pollutant concentrations increase in winters due to expanding combustion facilities. Results of bivariate correlation analysis showed that humidity and rainfall have remarkable negative correlations with the pollutants. Multiple linear regression models and artificial neural network (ANN) models were used to predict next day's PM10 and SO2 levels. In regression models calculated R2 values were 0.89 and 0.75 for PM10 and SO2, respectively. Among the various architectures, single layer networks provided better performance in ANN applications. Highest R2 values were obtained as 0.89 and 0.69 for PM10 and SO2, respectively, by using appropriate networks.