Abstract Conditional preference networks (CP-nets) express qualitative preferences over features of interest. A Boolean CP-net can express that a feature is preferable under some conditions, as long as all other features have the same value. This is often a convenient representation, but sometimes one would also like to express a preference for maximizing a set of features, or some other objective function on the features of interest. ASPRIN is a flexible framework for preferences in ASP, where one can mix heterogeneous preference relations, and this paper reports on the integration of Boolean CP-nets. In general, we extend ASPRIN with a preference program for CP-nets in order to compute most preferred answer sets via an iterative algorithm. For the specific case of acyclic CP-nets, we provide an approximation by partially ordered set preferences, which are in turn normalized by ASPRIN to take advantage of several highly optimized algorithms implemented by ASP solvers for computing optimal solutions. Finally, we take advantage of a linear-time computable function to address dominance testing for tree-shaped CP-nets