A supervisory hybrid system may be modeled from two levels: logic level(upper) and continuous level (lower). In this paper, adaptive fuzzy Petri nets and neural networks are combined together for supervisory hybrid system modeling. Adaptive Fuzzy Petri Net is adopted to model the supervisory logic parts, and dynamic neural networks are applied to continuous parts. Two hybrid system examples are illustrated to show the effective of the method.