In this paper we present a system for experimenting with combinations of dependency parsers. The system supports initial training of different parsing models, creation of parsebank(s) with these models, and different strategies for the construction of ensemble models aimed at improving the output of the individual models by voting. The system employs two algorithms for construction of dependency trees from several parses of the same sentence and several ways for ranking of the arcs in the resulting trees. We have performed experiments with state-of-the-art dependency parsers including MaltParser (Nivre et al., 2006), MSTParser (McDonald, 2006), TurboParser (Martins et al., 2010), and MATEParser (Bohnet, 2010), on the data from the Bulgarian treebank – BulTreeBank. Our best result from these experiments is slightly better then the best result reported in the literature for this language (Martins et al.,