Lexicographic Choice Logic (LCL) is a variant of Qualitative Choice Logic which is a logic-based formalism for preference handling. The LCL logic extends the propositional logic with a new connective (⃗⋄ ) to express preferences. Given a preference x⃗⋄ y, satisfying both x and y is the best option, the second best option is to satisfy only x, and satisfying only y is the third best option. Satisfying neither x nor y is not acceptable. In this paper, we propose a method for learning preferences in the context of LCL. The method is based on an adaptation of association rules based on the APRIORI algorithm. The adaptation consists essentially of using variations of the support and confidence measures that are suitable for LCL semantic.