In this work, classification of distributional data is addressed, where units are described by histogram-valued variables. The proposed approaches aim at extending the linear discriminant method developed for two-class classification to multiclass classification. This method is then applied to discrimination of network models. The goal is to identify the network model used to generate the networks, considering the distribution of four centrality measures.