An aging asset population and a less predictable volatile electricity consumption and production pattern urge DSOs to get insight in the condition of their medium voltage (MV) and low voltage (LV) networks. Because visual inspections of underground networks are impossible and the number of measurements is still very limited, this paper proposes a method to rank underground assets by looking for trends and patterns in historical outages with help of Machine Learning methods. Nine years of outages of MV and LV cables and joints in the network of a large Dutch DSO are analysed. A model is developed that couples each outage to the asset most probable responsible. Twentytwo different datasets are coupled with the asset database, ranging from load estimates of the asset to distance-to-a-railway. Each set could contain data that explains or correlates to some of the outages. Several Machine Learning techniques are benchmarked. The final model, created by the Random Forest algorithm, is applied to rank current assets. It is operational to determine the positioning of an online monitoring system in the DSO's MV network.