The recommendation architecture has been proposed as a system architecture which can enable a system to learn to perform a complex combination of interrelated functions. The capability of a system with the recommendation architecture to learn to manage complex telecommunication backbone networks has been investigated. A network model with a number of nodes and links and carrying realistic but randomly generated traffic was used as the target for the management system. Traffic data taken from the model was used as input to the recommendation architecture system. The traffic data was organized into inputs once every 5 minutes, and the management system organized these inputs into a hierarchy of repetition similarity. It was demonstrated that the outputs of this hierarchy provided information on the condition of the network. This output information was a compressed version of the inputs which correlated with major network conditions.
