This paper presents a method for detecting and nominating outliers based on the multihalver, or the delete-half jackknife. Since considering all possible half-samples is unpractical and unfeasable even for a moderate sample size, we present an algorithm for choosing a good set of half-samples. We also present an outlier detection method based on this algorithm. Simulations are given to show the effectiveness of our method and an example is also presented. © 2003 Elsevier B.V. All rights reserved.