This paper presents a computational model of association in design that incorporates the ability to learn from experience. Experiments with an implementation of our model of computational design association, the interpretation-driven model, demonstrate this experiential influence. The challenges inherent in building computational models of association, and the potential offered by the interpretation-driven approach are discussed with reference to a typology of association learning.