Making a pattern recognition decision with the maximum-likelihood rule is a particular case of the risk-based Bayesian decision rule which is simplified when the loss function is zero-one symmetrical and classes are equally a priori probable. In the case the recognition system is composed of several experts, we can take into account their estimated performance at the class level as a key heuristic-like factor to weight the loss function and drive the recognition process while fusing their decisions. Such indices are formally computed by applying the Discriminant Factor Analysis method. The experiments are carried out in the automatic language identification domain with a system composed of several identification experts. Fusion of expert decisions is achieved by building statistical classifiers.