In this paper, a new mining capability, called mining of substitution rules, is explored. A substitution refers to the choice made by a customer to replace the purchase of some items with that of others. The process of mining substitution rules can be decomposed into two procedures. The first procedure is to identify concrete itemsets among a large number of frequent itemsets, where a concrete itemset is a frequent itemset whose items are statistically dependent. The second procedure is then on the substitution rule generation. Two concrete itemsets X and Y form a substitution rule, denoted by X \triangleright Y to mean that X is a substitute for Y, if and only if (1) X and Y are negatively correlated and (2) the negative association rule X \to \overline Y exists. In this paper, we derive theoretical properties for the model of substitution rule mining. Then, in light of these properties, algorithm SRM (standing for substitution rule mining) is designed and implemented to discover the substitution rules efficiently while attaining good statistical significance. Empirical studies are performed to evaluate the performance of algorithm SRM proposed. It is shown that algorithm SRM produces substitution rules of very high quality.