The digitalization of nuclear power plants, with the rapid growth of information technology, opens the door to the development of new methods of condition-based maintenance. In this work, a semi-supervised method for characterizing the level of degradation of nuclear power plant components using measurements collected during plant operational transients is proposed. It is based on the fusion of selected features extracted from the monitored signals. Feature selection is formulated as a multi-objective optimization problem. The objectives are the maximization of the feature monotonicity and trendability, and the maximization of a novel measure of correlation between the feature values and the results of non-destructive tests performed to assess the component degradation. The features of the Pareto optimal set are normalized and the component degradation level is defined as the median of the obtained values. The developed method is applied to real data collected from steam generators of pressurized water reactors. It is shown able to identify degradation level with errors comparable to those obtained by ad-hoc non-destructive tests. Display omitted •A semi-supervised method for identifying the degradation level of industrial components is proposed.•The method is based on the three steps of feature extraction, evaluation and selection.•The extracted features performance is evaluated considering monotonicity, trendability and a novel correlability index.•The method is applied to fleets of steam generators in nuclear power plants.•The method identifies the component degradation level with performance comparable to those of non-destructive tests.