We consider the problem of clustering multiple networks into groups of networks with similar topology. A statistical model-based approach based on a finite mixture of stochastic block models is proposed. A clustering is obtained by maximizing the integrated classification likelihood criterion. This is done by a hierarchical agglomerative algorithm, that starts from singleton clusters and successively merges clusters of networks. As such, a sequence of nested clusterings is computed that can be represented by a dendrogram providing valuable insights on the data. We present results of our method obtained for a collection of foodwebs in ecology. We illustrate that the method provides relevant clusterings and that the estimated model parameters are highly interpretable and useful in practice.