It's one way, if you are assuming a regression structure and want outliers with respect to the regression model.
In general, you might not have a response variable, but you want to find outliers (for example, multivariate normal data with outliers).
In the non-regression context, one approach is to compute a robust center and a robust covariance for the data. You can then regard an outlier as a point that is more than a certain Mahalanobis distance from the center. See the section "Robust Multivariate Outlier Detection" on p. 7-9 of http://support.sas.com/resources/papers/proceedings10/329-2010.pdf
