Hi, Mine is a belated reply as I started specializing on statistics especially with SAS only recently. I reached this blog to solve a similar problem as you posed, fortunately with the help given by different members I manged to solve mine. This may be of help to you, provided you have not solved it for the last many years :).   proc univariate data=" " robustscale plot; var  varname; run;      This give you plethora of statistical values including robustscale and the outlier samples from among the given data as extreme values.  Here is a SAS-provided example of PROC UNIVARIATE and ROBUSTSCALE.   I initally calculated  q1, q3 and  iqr  to arrive at lower and upper bound values for outliers following "tukey" method. But, that didn't help me to filter out the outliers from the given data. On further exploration, I found proc univariate uses same "tukey" method to give lower and upper bound values in addition pinpointing the outliers. Very easy to follow.        See also these useful notes from @Rick_SAS: Yes, you can do MCD estimation with SAS. Here are a few articles: 1) Detecting Outliers in SAS: Part 3 2) "Rediscovering SAS/IML Software: Modern Data Analysis for the Practicing Statistician" (beginning on p. 7) 3) You might also enjoy reading "The curse of dimensionality: How to define outliers in high--dimensional data?" 4) Winsorization: the good, the bad, and the ugly Editor's note: this is a popular topic.  This original reply has been edited to incorporate multiple useful responses.     ... View more