This thesis presents the main results of the cost efficiency scores of 60 Brazilian electricity distribution utilities. Based on yardstick competition scheme, it was applied a Kohonen Neural Networks (KNN) to identify and to group the similar utilities. The KNN results are not deterministic, since the estimated weights are randomly initialized. Thus, a Monte Carlo simulation was used in order to find the most frequent clusters. Therefore was examined the use of the DEA methodology (input oriented, with and without weight constraints) and Bayesian and non- Bayesian Stochastic Frontier Analysis (centered on a Cobb- Douglas and Translog cost functions) to evaluate the cost efficiency scores of electricity distribution utilities. In both models the only input variable is operational cost (OPEX). The efficiency measures from these models reflect the potential of the reduction of operational costs of each utility. The outputs are the cost-drivers of the OPEX: the number of customers (a proxy for the amount of service), the total electric power supplied (a proxy for the amount of product delivered) and the distribution network size (a proxy of the customers scattering in the operating territory of each distribution utility). Finally, it is important to mention that these techniques can reduce the information assimetry to improve the regulator´s skill to compare the performance of the utilities in incentive regulation environments.