Emergent behaviour has become a plague of automation systems based on communication networks. Centralized monitoring of the network comes generally to late to sup-press unwanted behaviour. It is required to mark the ten-dency towards state changes in a decentralized manner. The paper discusses the role of local awareness by inspection of the model learning behaviour of feed-forward networks. The correlated movement of weight changes over time provides a clear indication of such profound changes, as demonstrated by some initial experience in industrial automation
