The evaluation of prostate cancer histology following hormonal therapy often represents a diagnostic problem for the pathologist. Previous studies have shown that an inference or Bayesian belief network (BBN) offers a descriptive classifier useful for the accurate analysis of morphological changes in individual cases of prostate neoplasia. Three different BBNs were evaluated in 94 cancer foci present in 20 radical prostatectomy (RP) specimens and in the matching biopsies in which the initial diagnosis of prostatic adenocarcinoma was made. Ten RP specimens were from patients treated with total androgen ablation or combination endocrine therapy (CET) before surgery. The first and second BBN allowed the identification with high certainty of the cancer foci present in the biopsies and RP specimens, as well as their Gleason grade, the belief value often being close to 1β’0. The results of the second BBN showed a good correspondence between the Gleason grade given in the biopsies and that in the RP specimens, except in the surgical material of the treated patients, in which upgrading was always present. The third BBN showed the existence of three subgroups in treated RP specimens, one with morphological effect, another with poor effect, and the third with the histology of untreated (i.e. unaffected) cancer. In conclusion, an inference network-based analysis allows the characterization of treated prostate cancers according to the degree of histopathological change.