Maximizing the realization of value from physical assets through asset management is a contemporary approach to support the achievement of organizational goals. Nevertheless, in face of the increase in complexity and pressure for better performance of their engineering systems, organizations are more dependent on the appropriate failure management policy selection for failure prevention. As these decisions take into account different aspects and performance of the physical assets, the decision makers should not rely on simple heuristics. Instead, these decisions are expected to be supported by systematic approaches that incorporate data analysis. Thus, it is suitable that organizations investigate how modern techniques such as machine learning can be incorporated in solving maintenance management challenges. In this context, this paper proposes a method to support the failure management policy selection in asset management based on the exploratory cluster analysis technique. The proposed method complies with three sections: acquisition of physical asset performance data, cluster analysis, and definition of failure management policies. The case study consists of the application of the method to support the maintenance strategies of a Brazilian hydroelectric power plant. This plant has been undergoing several studies for asset management improvements. The results obtained show a method by which organizations can define appropriate failure management policies according to determined groups of physical assets. This is an important result for maintenance management optimization as different maintenance tasks can be proposed to different engineering systems groups. Accordingly, this article is expected to contribute to asset management research and maintenance practitioners facing the challenge of defining the appropriate failure management policy to prevent failures in a portfolio of physical assets.