AbstractTwo methods for fog forecasting in Cuba are compared: use of the classical Fisher discriminant function, and the LVQ algorithm developed by the Laboratory of Computer and Information Sciences, Helsinki University of Technology. The relative number of correct forecasts is over 70% for both, which can be considered a good performance. When the learning sample is large enough and nearly equi‐probabalistic, the LVQ algorithm provides a greater number of correct forecasts than those obtained via the Fisher discriminant function. However, the results attained via the LVQ algorithm are not steady when the learning sample is far from being equi‐probabalistic, because the number of fog cases is much reduced. Until larger samples are available for some regions, it will be necessary to use both methods for fog forecasting in C