The forecasting system for Today`s and Tomorrow`s PM10 was developed based on the statistical model and the forecasting was performed at 9 AM to predict Today`s 24 hour average PM10 concentration and at 5 PM to predict Tomorrow`s 24 hour average PM10. The Today`s forecasting model was operated based on measured air quality and meteorological data while Tomorrow`s model was run by monitored data as well as the meteorological data calculated from the weather forecasting model such as MM5 (Mesoscale Meteorological Model version 5). The observed air quality data at ambient air quality monitoring stations as well as measured and forecasted meteorological data were reviewed to find the relationship with target PM10 concentrations by the regression analysis. The PM concentration, wind speed, precipitation rate, mixing height and dew-point deficit temperature were major variables to determine the level of PM10 and the wind direction at 500 hpa height was also a good indicator to identify the influence of long-range transport from other countries. The neural network, regression model, and decision tree method were used as the forecasting models to predict the class of a comprehensive air quality index and the final forecasting index was determined by the most frequent index among the three model`s predicted indexes. The accuracy, false alarm rate, and probability of detection in Tomorrow`s model were 72.4%, 0.0%, and 42.9% while those in Today`s model were 80.8%, 12.5%, and 77.8%, respectively. The statistical model had the limitation to predict the rapid changing PM10 concentration by long-range transport from the outside of Korea and in this case the chemical transport model would be an alternative method.