The occurrence of fog can significantly impact air transport operations, and plays an important role in aviation safety. The economic value of aviation forecasts for Sydney Airport alone in 1993 was estimated at $6.8 million (Australian dollars) for Quantas Airways. The prediction of fog remains difficult despite improvements in numerical weather prediction guidance and models of the fog phenomenon. This paper assesses the ability of artificial neural networks (ANNs) to provide accurate forecasts of such events at Canberra International Airport (YSCB). Unlike conventional statistical techniques, ANNs are well suited to problems involving complex nonlinear interactions and therefore have potential in application to fog prediction. A 44-yr database of standard meteorological observations obtained from the Australian Bureau of Meteorology was used to develop, train, test, and validate ANNs designed to predict fog occurrence. Fog forecasting aids were developed for 3-, 6-, 12-, and 18-h lead times from 0600 local standard time. The forecasting skill of various ANN architectures was assessed through analysis of relative operating characteristic curves. Results indicate that ANNs are able to offer good discrimination ability at all four lead times. The results were robust to error perturbation for various input parameters. It is recommended that such models be included when preparing forecasts for YSCB, and that the technique should be extended in its application to cover other similarly fog-prone aviation locations.