A methodological approach of pattern recognition is proposed to make on-line selection of cereal products as they are poured down through the air. It addresses the characterization of product populations according to different pre-defined quality classes using image analysis. In order to take into account the complexity of this problem, we propose to use both global (related to the whole image) and individual (related to each particle) variables. The aim of the data processing module is to reduce the dimension of the variable space without losing information, and then to select the most pertinent components to train the decision system. The first stage of this system, called generalist one, has to give an ambiguous response, that means to select a subset of possible output classes. The second, or specialist, which is trained to distinguish only some subjects of classes delivered by the generalist one, gives the decision. The method has been applied in the framework of milling products classification. Three quality classes have been defined, they correspond to the rolls gap (0.30, 0.40, 0.50 mm.) of the first break rolls of a semolina pilot mill. In these conditions, the classification accuracy rate achieved by the system is higher than 80%.