As the biological behaviour of even early stage renal cell cancer (RCC) strongly correlates with tumor size, it has been argued that the inclusion of RCC up to a maximum diameter of 7 cm into a common subgroup classified as T1 according to the 5th edition of the TNM system would not adequately represent the different biological aggressiveness of these malignancies. Taking this into account, the TNM classification, which now categorizes T1 RCC as T1a and T1b according to a cutoff size of 4 cm, was recently modified. However, only a few larger investigations, mainly based on univariate statistical analyses, that support the suitability of this cutoff are at present available from the literature. Therefore, it was the aim of the present investigation to determine the tumor size that best separates patients with low responses from those with high risk for tumor progression by univariate (log rank test) and multivariate (Cox regression model) statistical analyses. Between 1981 and 2000, 652 patients (443 males and 209 females) underwent tumor nephrectomy in our clinic for the diagnosis of RCC. Of these, 243 patients revealed primary tumors with a local growth not extending beyond the renal capsula at the time of surgery. For the different cutoff levels (starting from 2 cm in increments of 1 cm up to 8 cm) that were selected to subdivide the patients into groups according to the maximum tumor diameter, the correlation between tumor size and overall survival was determined by univariate and multivariate statistical analyses. It became evident that although during univariate analysis the prognostic value of a cutoff size of 4 cm was confirmed, multivariate analysis identified the highest relative risk for cause-specific death (2.93) for patients having tumors larger than 5 cm in maximum diameter. Therefore, the 5 cm cutoff seems to best determine the clinical prognosis of patients undergoing tumor nephrectomy for early stage RCC. The present study demonstrates the need for multivariate statistical approaches when the latest modification of the TNM classification system is critically evaluated.