The detection of outliers in multivariate models is always a dicult matter, but the subject is even more complex when dealing with dependent structures, as it is the case with the Simultaneous Equation Model (SEM). Unlike other models dened by systems of equations, such as the multivariate regression, the SEM assumes that the response variable in each equation can be stated as an explanatory variable in the rest of the system, meaning that explanatory variables can be correlated with the error terms. We present a method of outlier detection that bypasses those diculties using the asymptotic distribution of adequate robust Mahalanobis distances. The process identies anomalous data points as outliers of the SEM in simple steps and it provides a clear visualization. We illustrate this procedure with a real econometric data set.