The paper describes an attempt for the 24-hours prediction of photochemical pollutant levels using neural network models. Two models are developed for this purpose that relate peak pollutant concentrations to meteorological and emission variables. The analysis is based on measurements of O3 and NO2 from the city of Athens. The selected input meteorological variables fulfil two criteria: (a) cover atmospheric processes that determine the dispersion and diffusion of the airborne pollutants and (b) are available from routine observations or forecasts. The comparison between model predictions and actual observations shows very good agreement.