The selection of Maintenance Significant Items (MSI) is undoubtedly one of the most important phases in the implementation of Reliability-Centered Maintenance (RCM) in any organization, being essentially a screening phase in which the number of items for analysis can be reduced and prioritized. Despite its importance, there are currently few studies that present systematic and structured methods for the identification of MSI. Fundamentally, there are two phases to identify the MSI in the physical asset portfolio. First, based on the system study and analysis, the criteria and scales are established. This phase can be carried out with the support of standards or experts’ knowledge, and objective criteria and scales can be obtained. Second, the criteria evaluation for each item is performed. In this phase, generally, a multicriteria decision method is used to rank the most critical items, such as Analytic Hierarchy Process (AHP) and Analytic Network Process (ANP). However, their intrinsic subjective evaluation can lead to bias results. To prevent this issue and simplify the process of defining MSI, this work proposes the use of an unsupervised method based on Principal Component Analysis (PCA). From the application of this method, not only are the MSI of a system defined, but the importance of the criteria selected in the first phase is assessed based on the variability of the scores associated with each item. Thus, criteria that end up not influencing the result of the criticality assessment can be disregarded from a minimum value of the cumulative percentage variation. To demonstrate the method, it is implemented in a Brazilian hydroelectric power plant and the results are compared to those obtained from a more traditional approach based on AHP. It is noted that the proposed method points to a robust MSI selection, consistent with the analyzed system.