In this paper, a novel data mining method is introduced to solve the multi-objective optimization problems of process industry. A hyperrectangle association rule mining (HARM) algorithm based on support vector machines (SVMs) is proposed. Hyperrectangles rules are constructed on the base of prototypes and support vectors (SVs) under some heuristic limitations. The proposed algorithm is applied to a simulated moving bed (SMB) paraxylene (PX) adsorption process. The relationships between the key process variables and some objective variables such as purity, recovery rate of PX are obtained. Using existing domain knowledge about PX adsorption process, most of the obtained association rules can be explained.