In the field of remote sensing (RS) image classification, pattern indeterminacy due to inherent data variability is always present. Class mixture, too, is a serious handicap to conventional classifiers in order to settle proper class patterns. Fuzzy classification techniques improve the extraction of information yielded by conventional methods, i.e., statistical classification procedures, because both in the design of the classifier and when bringing out classification results, natural fuzziness present in real-world recognition processes is considered. This paper presents first the application of a fuzzy classification algorithm from Kent and Mardia to RS images, along with the analysis of the results and comparison against `hard' classifications. Secondly, we put forward one particular method to display these results (fuzzy partitions) by coding pixels' membership into a pseudocolor representation. This representation is intended to serve as an interface between fuzzy coefficients resulting from the classification process and a very natural way for humans to perceive information such as that of color mixtures.