To maintain a reliable and stable power supply, operators of electrical power distribution networks regularly inspect electrical components of the distribution networks. Inspection results and necessary measures corresponding to them have been stored electronically, and there is lively discussion how to utilize these electronic records for operations and planning of the distribution networks. The authors propose a decision support method that utilizes the accumulated records to predict the necessity of replacing electrical components installed in a distribution network. In the proposed method, decision tree learning analyzes the inspection results of the electrical components that have already been replaced and identifies the criteria by which the operators decided to replace them. The resulting tree-like model, which is the decision tree, analyzes the newly input inspection result and determines if the target component needs to be replaced. Using actual inspection and maintenance records, numerical simulations were carried out. In the numerical simulations, the authors’ proposal showed reasonable prediction accuracy despite applying a simple decision tree learning.