Possibilistic networks are important tools for dealing with uncertain pieces of information. For multiply-connected networks, it is well known that the inference process is a hard problem. This paper studies a new representation of possibilistic networks, called hybrid possibilistic networks. The uncertainty is no longer represented by local conditional possibility distributions, but by their compact representations which are possibilistic knowledge bases. We show that the inference algorithm in hybrid networks is strictly more efficient than the ones of standard propagation algorithm.