In this study, a novel decision fusion scheme for the classification of respiratory sounds is proposed. Furthermore a regularization scheme is applied to the data to stabilize training and consultation. The method consists of dividing respiratory cycles of patients into phases, and classifying each phase with a separate multilayer perceptron, called the "phase expert". Each phase information consists of several time segments and their parametric representation. Expert decisions on phase segments are then combined by a decision fusion scheme, simulating a consultation session.