Many asset owners and maintainers have the ambition to better predict the future state of their equipment to make timely and better-informed maintenance decisions. Although many methods to support high-level maintenance policy selection are available, practitioners still often follow a costly trial-and-error process in selecting the most suitable predictive maintenance method. To address the lack of decision support in this process, this paper proposes a framework to support asset owners in selecting the optimal predictive maintenance method for their situation. The selection framework is developed using a design science process. After exploring common difficulties, a set of solutions is proposed for these identified problems, including a classification of the various maintenance methods, a guideline for defining the ambition level for the maintenance process, and a classification of the available data types. These elements are then integrated into a framework that assists practitioners in selecting the optimal maintenance approach. Finally, the proposed framework is successfully tested and demonstrated using four industrial case studies. It can be concluded that the proposed classifications of ambition levels, data types and types of predictive maintenance methods clarify and accelerate the complex selection process considerably.