In recent years, many classification models have been developed and applied to increase their accuracy. The concept of distance between two samples or two variables is a fundamental concept in multivariate analysis. This paper proposed a tool that used different similarity distance approaches with ranking method based on Mean Average Precision (MAP). In this study, several similarity distance methods were used, such as Euclidean, Manhattan, Chebyshev, Sorenson and Cosine. The most suitable distance measure was based on the smallest value of distance between the samples. However, the real solution showed that the results were not accurate as and thus, MAP was considered the best approach to overcome current limitations.