The algorithms for decision tree induction are investigated, and their limitations are analyzed. A new probability-based algorithm of decision tree induction, PID, is presented. Similar to ID3, PID selects attributes according to information gain, but PID is able to merge branches by using probability-based clustering. The experimental results show that the recognition rate gained by PID is better than that by ID3.