Due to increasing reliance on computer communication networks, it is highly desirable that networks should have the ability to detect symptoms of oncoming exception conditions and take measures to prevent them thereby enabling a degree of proactive network management that underpins an acceptable quality of service. This paper proposes a framework for achieving congestion avoidance through proactive network management using data mining. It examines the inter-relationships between network element management information base (MIB) attributes, queue parameters (associated with a transmission link) and the level of congestion at a network node and identifies hybrid parameters that have a bearing on congestion. By employing data mining on the data pertaining to these variables, congestion at the network node can be predicted. Results from our initial experimentation with particular data mining models show that the accuracy achieved is as high as 98% in all of the cases thus rendering data mining a viable approach to proactively identity network exception conditions.