Strategic use of condition monitoring to support rehabilitation planning of the Oslo wastewater network is the topic presented. The paper describes the process followed from investigating the quality of the data, selecting the final dataset for models calibration and finally modeling the prediction of the deterioration process under selected rehabilitation strategies. The model applied for the analysis is termed GompitZ and it based on the probabilistic theory of Markov chains; it defines the relationship between the current state and the expected service time of sewer pipes using Close Circuit TV inspections as classification input. The research highlighted a major issue: the need to review the Norwegian standard used to classify pipes from visual inspection. The problem consists on the standard being too pessimistic in the pipe classification leading to a much more negative figure of the overall network need for rehabilitation that it is in reality. By classifying the pipes as much more close to collapse than thy actually are, brings also to a too high estimation of the investment needs for rehabilitation plans. The research is currently focused on further developing the GompitZ tool by introducing the concept of ‘risk’ in the ranking of segments for prioritizing rehabilitations in long-term simulations. The choice of the strategy to undertake would be then based on a trade-off between costs, conditions and risk.