Beta regression model is a widely known statistical model when the response variable has the form of fractions or percentages. The maximum likelihood method is usually employed for estimating the regression coefficients of beta regression model. However, the maximum likelihood estimator is highly sensitive to outliers. To solve this problem, new statistical techniques have been developed that are not so easily affected by outliers; these are robust methods. This paper discusses the efficiency of using four robust estimation methods, the S-estimation method, MM-estimation method, least trimmed sum of absolute deviation method and the robust and efficient weighted least squares estimation method in estimating the parameters of beta regression model in the presence of outliers. A Monte Carlo simulation study is performed to compare the performance of these robust estimators with the maximum likelihood estimator. Also, a real data set is used to illustrate the applicability of these estimators. The results showed that the robust and efficient weighted least squares estimation method gives better performance than the maximum likelihood estimation, S-estimation, MM-estimation, and least trimmed sum of absolute deviation methods.