The various grading systems proposed for renal cell carcinomas all suffer from problems related to inter-observer variability. Some of these grading systems are based, either partially or wholly, on morphonuclear criteria, such as nuclear size and shape, anisonucleosis, and chromatin pattern. These criteria can be quantitatively (and thus objectively) evaluated by means of the computer-assisted microscopic analysis of Feulgen-stained nuclei. In the present work, 39 quantitative variables, including two morphometric, 28 chromatin pattern-related, and nine DNA ploidy level-related, were computed for 65 renal cell carcinomas. The actual diagnostic information contributed by each variable was determined by means of multifactorial statistical analysis (discriminant analysis) and two artificial intelligence-related methods of data classification (the decision tree and production rule methods). The results show that quantitative information, as provided by the computer-assisted microscopy of Feulgen-stained nuclei and analysed by means of artificial intelligence-related methods of data classification, contributes significant diagnostic information for the grading of renal cell carcinoma, thus reducing the problem of inter-observer reproducibility.