Aiming to meet the criteria of availability and reliability required by the rules of the Brazilian electric sector, energy companies have increased the use of systems for monitoring and diagnosis. This allows the identification of prior evidence of abnormalities in the operational condition of substation equipment, enabling the practice of predictive maintenance and asset management based on risk assessment. The RCM (reliability centered maintenance) is a process used for planning of maintenance and its main objective is the pro-active management rather than indiscriminate prevention of all failures. The aim is the identification of functional failures, failure modes of components, effects and consequences of these failures to develop a maintenance plan for each mode of failure in order to eliminate it or reduce its consequences. Classification of identified failure modes is extremely important, since it indicates the priority for the decision making process, leading to a technical and cost effective maintenance. The main objective of this dissertation is to develop and implement a methodology for prioritization of failure modes of functional equipment in substations of electric power, obtained by applying the RCM process. Assuming a multicriteria context, to rank the modes of failure, as opposed to the empirical methods adopted today, it is proposed the multicriteria decision PROMETHEE and fuzzy-PROMETHEE methodologies. In the fuzzy-PROMETHEE the input data are treated as fuzzy numbers, with the purpose of considering the uncertainty contained in the data. Using the fuzzy-PROMETHEE ranking process, a more realistic modes of failure is obtained. The severity of the effects associated with the occurrence of each mode of failure was used as a criterion for evaluating the methodology developed. It is known that methods of functional failures affect businesses in different ways and may compromise the reliability of the system, operating costs, or even the safety or the environment. Therefore, different degrees of severity were attributed to the economic, operational, environmental, and safety impacts. A fuzzy inference system was developed to obtain the overall severity of each fault mode, where the inputs are the specific severities mentioned above. With the overall severity of each mode of failure it is possible to obtain the risks associated with each mode of failure. From the ranking of failure modes, a methodology can be applied to prioritize actions that are more appropriate for the reduction or elimination of the consequences of each mode of failure. The major contribution of this work is the availability of a refined model for a maintenance plan aimed at increasing the operational reliability of equipment and reduction of costs, considering multiple criteria analysis and the interests of different decision makers.