The problem of products missing from the shelf is a major one in the grocery retail sector, as it leads to lost sales and decreased consumer loyalty. Yet, the possibilities for detecting and measuring an "out-of-shelf" situation are limited. In this paper we suggest the employment of machine-learning techniques in order to develop a rule-based Decision Support System for automatically detecting products that are not on the shelf based on sales and other data. Results up-to-now suggest that rules related with the detection of "out-of-shelf" products are characterized by acceptable levels of predictive accuracy and problem coverage.