We describe a model for decision making for bone marrow immunophenotypic analysis of acute leukemia. In this study, we used decision tree induction as an information processing system for the analysis of flow cytometry immunophenotype results of bone marrow specimens obtained for the diagnosis of acute leukemia. By using decision tree analysis, we queried which antibodies and at what percentage cut-offs led to particular diagnoses. Flow cytometry results of up to 27 monoclonal antibodies from bone marrow specimens of 175 adult and pediatric cases were used: acute lymphoblastic leukemia (n= 80), myeloid leukemia (n = 44), mixed lineage (n= 16), and reactive marrow (n= 35). The percentage of positive cells was used as input data, and the diagnoses were used as output of the information processing system. Results of the decision tree showed an easy, accurate, and intuitive algorithm that can delineate a hierarchy of antibodies relevant to diagnosis. A correct discrimination of acute myeloid and lymphoid leukemia from benign bone marrow can be inferred by using the results of four to eight from a panel of up to 27 antibodies with an accuracy of 95%. Here, we describe a computer-aided model that uses decision tree induction applied to flow cytometry immunophenotype data. If generalizable, this technique may be an alternative approach to modeling complex information like that seen in hematopathology and may complement the immunologist\'s interpretation, along with cytochemistry and morphology results, in the diagnosis of acute leukemia.