In this paper, a hybrid learning approach named hybrid decision tree (HDT) is proposed. HDT simulates human reasoning using symbolic leaming to do qualitative analysis and using neurallearning to do subsequent quantitative analysis. It generates the trunk of a binary HDT according to the binary inormation gain r atio critetion in an instance space definde by only original unordered attributes. If unordered attributes cannot further distingguish training examples falling into a leaf node whose diversity is beyond the diversity-threshold, then the node is marked as a dummy node. After all those dummy nodes are marked, a speific feedforward neural netword namde FANNC that is trainde in an instance space definde by only original ordered attributes is exploited to accomplish the leaming task. Moreover, this paper distinguishes three kinds of inremental learning tasks. Two incremental leaming procedures designde for example-incremental learning with different storage requirements are provided, which enables HDT to deal gracefully with data sets where new data are freaquently appended. Also a hypothesis-driven constructive induction mechanism is provided, which enables HDT to generate compact concept descriptions.