Using a series of 105 renal cell carcinomas (RCCs) we investigated whether features quantitatively describing the appearance of Feulgen-stained nuclei and, more particularly, of their chromatin (on the basis of computer-assisted microscopy) can contribute any significant prognostic information. Thirty morphonuclear and 8 nuclear DNA content-related variables were thus generated. The actual prognostic values of this set of cytometric variables was compared (by means of discriminant statistical analysis) to conventional diagnostic and/or prognostic markers including histopathological grades, tumour invasion levels and the presence or absence of metastases. We obtained complete clinical follow-ups for 49 of the 105 RCC patients under study, making it possible to define a subset of patients with a bad prognosis (i.e., who died in the 12 months following nephrectomy) and a subset of patients with a good prognosis (i.e., who survived at least 24 months following nephrectomy). An original method of data analysis related to artificial intelligence (decision tree induction) enabled a strong prognostic model to be set up. In the case of 10 new patients, this model identified all the dead patients as having a bad survival status, with a total of 8 correct predictions. Another prognostic model similarly generated enabled the correct predictions to be confirmed.