In this paper we propose a method for creation of rules for images classification using fuzzy expert systems. The method consists of the analysis of the results of clusters formed by the application of a biased clustering algorithm to the image pixels. Biased clustering algorithms are partially supervised classification algorithms which allows the use of imprecise, incomplete or conflicting expectancies of assignment of data points to classes, and by iterative clustering attempts to solve the conflicts and incompleteness and obtain labeled clusters. The resulting clusters can be used to create new rules or membership functions which can lead to more and/or better rules for classification of the data using a fuzzy expert system. The new rules and membership functions can also be compared with the ones used to create the original expectancies of assignment of data for validation. Examples of application of the proposed method to synthetic and image data are presented. The classification results are evaluated and compared, conclusions on the problems, advantages and overall features of the proposed methods are presented and future work directions are considered.