In real world the knowledge used for aiding decision-making is always time varying. Most existing data mining approaches assume that discovered knowledge is valid indefinitely. Temporal features of the knowledge are not taken into account in mining models or processes. As a consequence, people who expect to use the discovered knowledge may not know when it became valid or whether it is still valid. This limits the usability of discovered knowledge. In this paper, temporal features are considered as important components of association rules for better decision-making. The concept of temporal association rules is formally defined and the problems of mining these rules are addressed. These include identification of valid time periods and identification of periodicities of an association rule, and mining of association rules with a specific temporal feature. A system has been designed and implemented for supporting the iterative process of mining temporal association rules, along with an interactive query and mining interface with an SQL-like mining language.