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This article presents an approach to automatic language classification by means of linguistic networks. Networks of 11 languages were constructed from dependency treebanks, and the topology of these networks serves as input to the classification algorithm. The results match the genealogical similarities of these languages. In addition, we test two alternative approaches to automatic language classification – one based on n-grams and the other on quantitative typological indices. All three methods show good results in identifying genealogical groups. Beyond genetic similarities, network features (and feature combinations) offer a new source of typological information about languages. This information can contribute to a better understanding of the interplay of single linguistic phenomena observed in language. [ABSTRACT FROM PUBLISHER]