Our research is intended to develop ultrasonic methods for characterizing cancerous prostate tissue and thereby to improve the effectiveness of biopsy guidance, therapy targeting, and treatment monitoring. We acquired radio-frequency (RF) echo-signal data and clinical variables, e.g., PSA, during biopsy examinations. We computed spectra of the RF signals in each biopsied region, and trained neural network classifers with over 3,000 sets of data using biopsy data as the gold standard. For imaging, a lookup table returned scores for cancer likelihood on a pixel-by-pixel basis from spectral-parameter and PSA values. Using ROC analyses, we compared classification performance of artificial neural networks (ANNs) to conventional classification with a leave-one-patient-out approach intended to minimize the chance of bias. Tissue-type images (TTIs) were compared to prostatectomy histology to further assess classification performance. ROC-curve areas were greater for ANNs than for the B-mode-based classification by more than 20%, e.g., 0.75 +/- 0.03 for neural-networks vs. 0.64 +/- 0.03 for B-mode LOSs. ANN sensitivity was 17% better than the sensitivity range of ultrasound-guided biopsies. TTIs showed tumors that were entirely unrecognized in conventional images and undetected during surgery. We are investigating TTIs for guiding prostrate biopsies, and for planning radiation dose-escalation and tissue-sparing options, and monitoring prostrate cancer.