The present paper is devoted to the pattern recognition procedures that simultaneously use the information contained in the empirical data (learning set) and the set of expert rules with unprecisely formulated weights understood as conditional probabilities. Adopting the probabilistic model the combined and unified recognition algorithms are derived. In the first approach algorithm is based simply on the both set of data, in the second however, one set of data is transformed into the second one. Proposed algorithms were applied practically to the diagnosis of acute renal failure in children. Obtained results have proved its effectiveness in the computer medical decision-making.