The temperature forecasts of the Advanced Regional Prediction System are postprocessed by a neural network. Specifically, 31 stations are considered, and for each a neural network is developed. The nine input variables to the neural network are forecast hour, model forecast temperature, relative humidity, wind direction and speed, mean sea level pressure, cloud cover, and precipitation rate and amount. The single dependent variable is observed temperature at a given station. It is shown that the model temperature forecasts are improved in terms of a variety of performance measures. An average of 40% reduction in mean-squared error across all stations is accompanied by an average reduction in bias and variance of 70% and 20%, respectively.