The Minimum Distance to Mean method is a supervised classification method that first analyzes the training data you provide and calculates a mean vector for each prototype class, described by the class center coordinates in feature space. The Minimum Distance to Mean algorithm then determines the Euclidean distance from each unclassified cell to the mean vector for each prototype class and assigns the cell to the closest class. This method has no user-defined parameters. The process can create such an optional distance raster for the applications. The Minimum Distance to Mean algorithm is mathematically simple and efficient, but it does not recognize differences in the variance of classes, which determines their relative "size" in feature space. For training sets in which prototype classes with different variance lie close to each other in feature space, data points near the edge of a "larger" class may be closer to the center of a nearby "smaller" class than to their own class center, resulting in miss-classification of some unknown cells. For this reason, the Minimum Distance to Mean method works best in applications where spectral classes are dispersed in feature space and have similar variance. This paper examines the classification problem with approach of Minimum Euclidian Distance to Mean algorithm.