The aim of the presented system is simplification of the daily pathological routine of prostatic cancer diagnostics. The system combines telepathology with computer-aided diagnostics algorithms. To the best of our knowledge, this is the first approach proposing such a comprehensive method. Our system is designed to accumulate knowledge in learning process during diagnostics. Our system targets image acquisition and interpretation stages. The image acquisition subsystem solves various problems related to microscopical slide digitization like biomedical image registration, data representation, and processing. The interpretation subsystem bases on Gabor filter texture features as well as on color features. A support vector machine classifier together with feature selection is used for computer-aided diagnostics. The experimental validation of the system bases on a database of more than three thousand samples.During the experimental evaluation, the system exhibited successful interaction with a pathologist.