The kev issues in network management are the representation and sharing of management information and the automatic management mechanisms based on the underlying information infrastructure. In this paper, we propose a framework, which operates on the standard MIB’s and CMIP, for the network management system with learning and inference as its management engines. In addition to the general domain knowledge, patterns related to the managed network are learned to enhance the understanding of the network and refine the knowledge base. Facts in object-oriented databases or queries from management applications trigger the inference process on logical rules which are either prespecified knowledge or learned network patterns. Forward inference drives prediction and control, while backward inference directs diagnosis and supports view abstraction. A case study on ATM network topology tuning is presented.