Multisource classification methods based on neural networks, statistical modeling, genetic algorithms, and fuzzy methods are considered. For most of these methods, the individual data sources are at first treated separately and modeled by statistical methods. Then several decision fusion schemes are applied to combine the information from the individual data sources. These schemes include weighted consensus theory where the weights of the individual data sources reflect the reliability of the sources. The weights are optimized in order to improve the combined classification accuracies. The methods are applied in the classification of a multisource data set, and the results compared to accuracies obtained with conventional classification schemes.