A linear multiple regression equation was developed for each of 27 ozone monitoring sites in the north-eastern United States to forecast the next day's maximum 1 h average ozone concentration. Thirty-five prognostic meteorological variables, the climatological daily maximum surface temperature, the length and direction of 12 and 24 h backward trajectories, and three air quality variables relating to the seasonality or the upwind ozone concentrations were considered as possible predictors in each of the regression equations. Data pertaining to 244 randomly selected days formed the developmental or the dependent data set, while the data pertaining to the remaining 122 days in the months of June, July, August and September of 1975, 1976 and 1977 were used to assess the performance of the regression equations. Performance was assessed and compared to that of persistence, via statistical evaluations of site-specific forecasts. In addition, areas of the Northeast where the 1 h ozone standard was predicted to be exceeded, were compared to the areas where the standard was exceeded. The results indicated that approximately half of the predictions generated from the independent data set were within 20% of the observations, while 77% were within 40% of the observations. A tendency for the underprediction of the maximum concentrations was noted. Overall, the regression equations performed best in forecasting the trends and patterns of the daily 1 h average ozone concentrations.