In this work we present a backjumping technique for Disjunctive Logic Programming (DLP) under the Answer Set Semantics. It builds upon related techniques that had originally been proposed for propositional satisfiability testing, which have been adapted to non-disjunctive Answer Set Programming (ASP) recently [1, 2]. We focus on backjumping without clause learning. We provide a new theoretical framework for backjumping on Disjunctive Logic Programs. We optimize the reason calculus, reducing the information to be stored, while fully preserving the correctness and the efficiency of the jumping technique. We implement the proposed technique in DLV, the state-of-the-art DLP system. We have conducted several experiments on hard random problems in order to assess the impact of backjumping. Our conclusion is that when lookahead is employed, there is basically no advantage when enabling backjumping. However, when lookahead is disabled, we can observe that the number of choices in general decre...