AbstractWe present an overview of the major developments in the area of detection of outliers. These include projection pursuit approaches as well as Mahalanobis distance‐based procedures. We also discuss principal component‐based methods, since these are most applicable to the large datasets that have become more prevalent in recent years. The major algorithms within each category are briefly discussed, together with current challenges and possible directions of future research. Copyright © 2009 John Wiley & Sons, Inc.This article is categorized under:Statistical and Graphical Methods of Data Analysis > Robust Methods