The present work introduces an approach to predict the nitrogen oxides (NOx) emission characteristics of a large capacity pulverizedcoal fired boiler with artificial neural networks (ANN). The NOx emission andcarbon burnout characteristics were investigatedthroug h parametric fieldexperi ments. The effects of over-fire-air (OFA) flow rates, coal properties, boiler load, air distribution scheme and nozzle tilt were studied. On the basis of the experimental results, an ANN was used to model the NOx emission characteristics andthe carbon burnout characteristics. Comparedwi th the other modeling techniques, such as computational fluid dynamics (CFD) approach, the ANN approach is more convenient and direct, and can achieve good prediction effects under various operating conditions. A modified genetic algorithm (GA) using the micro-GA technique was employedto perform a search to determine the optimum solution of the ANN model, determining the optimal setpoints for the current operating conditions, which can suggest operators' correct actions to decrease NOx emission.