|Multiple Attribute Decision Making (MADM) is an evaluation method often used by decision makers and widely used in many management areas. Several methods have been proposed for solving MADM. However, a major criticism of MADM is that different techniques may yield different results when applied to the same problem. In this paper, we run a simulation study using the number of alternatives and criteria, and choices of weights as the input parameters. The data sets were generated using exponential distributions. We investigate the performances of five methods: Simple Additieve Weighting (SAW), Hierarchical Additive Weighting (HAW), ELECTRE, TOPSIS and Grey Relational Analysis (GREY) using seven measures of performance, including Mean Squared Error, Mean Absolute Error, top rank matched count, number of rank matched, weighted rank crossing 1, weighted rank crossing 2, and Spearman’s correlation for ranks. The results show that, the solutions provided by SAW was used as the benchmark, ELECTRE behaves closer to SAW, followed by GREY, with TOPSIS the least similar to SAW. Furthermore, the cluster analysis shows that, we can cluster the five methods into two groups, with methods in each group yielding similar results.