A comparison was conducted of several discriminant models (linear, stepwise linear and quadratic) using two definitions of prior probability (proportional and equal) to detect alcoholism on the basis of routine blood test results. Discriminant functions were derived on a sample of men alcoholic (N = 407) and nonalcoholic (N = 1068) psychiatric patients, and were cross-validated on an independent sample of the same two populations (Ns = 365 and 1020, respectively). Linear discriminant models generally outperformed quadratic models. The best classification was obtained by the equal stepwise linear model that retained SGOT, calcium, albumin, inorganic phosphate and BUN. The best quadratic model (equal) achieved good overall accuracy but weak sensitivity. The linear model was relatively accurate in terms of classification, and better sensitivity was achieved with the five best predictors than with all available measures.