In this paper, the robustness of the least distances (LD) estimate in multivariate linear models, as defined by Bai, Chen, Miao and Rao (1990), is discussed in terms of the influence function as well as the breakdown point. The LD estimate is shown to be more robust than the least squares (LS) estimate. The robustness of the LD is similar to that of the least absolute deviations (LAD) estimate, a well studied robust estimate in the univariate case. In particular, if there are no outliers in the design matrices, the breakdown point of the LD estimate reaches the highest value, 1/2.