Analysis and forecasting of air quality parameters are important topics of atmospheric and environmental research today due to the health impact caused by air pollution. As one of major pollutants, ozone, especially ground level ozone, is responsible for various adverse effects on both human being and foliage. Therefore, prediction of ambient ozone levels in certain environment, especially the ground ozone level in densely urban areas, is of great importance to urban air quality and city image. To date, though several ozone prediction models have been established, there is still a need for more accurate models to develop effective warning strategies. The development of such models is difficult because the meteorological variables and the photochemical reactions involved in ozone formation are very complex. The present work aims to develop an improved neural network model, which combines the adaptive radial basis function (ARBF) network with statistical characteristics of ozone in selected specific areas, and is used to predict the daily maximum ozone concentration level. The improved method is trained and testified by hourly time series data collected at three air pollutant-monitoring stations in Hong Kong during 1999 and 2000. The simulation results demonstrate the effectiveness and the reliability of the proposed method.