Decision trees induction algorithms have been used for classification in a wide range of application domains. In the process of constructing a tree, the criteria of selecting test attributes will influence the classification accuracy of the tree. In this paper, the degree of dependency of decision attribute to condition attribute, based on rough set theory, is used as a heuristic for selecting the attribute that will best separate the samples into individual classes. The result of an example shows that compared with the entropy-based approach, our approach is a better way to select nodes for constructing decision trees.