A cost-sensitive decision tree is induced for the purpose of building a decision tree from training data that minimizes the sum of the misclassification cost and test cost. Although this problem has been investigated extensively, no previous study has specifically focused on how the decision tree can be induced if the classification task must be completed within a limited time. Accordingly, we developed an algorithm to generate a time-constrained minimal-cost tree. The main idea behind the algorithm is to select the attribute that brings the maximal benefit when time is sufficient, and to select the most time-efficient attribute (i.e., the attribute that provides maximal benefit per unit time) when time is limited. Our experimental results show that the performance of this algorithm is highly satisfactory under various time constraints across distinct datasets.