Traditional checkout systems are labor-intensive and can be a great source of frustration for customers when having to wait in line. In contrast, automated checkout systems scan, total and charge a customer’s purchase to a registered payments account while they are simply leaving the store. We focus on the main challenge of automatically detecting customer purchases. To this end, we develop a checkout system that leverages data mining techniques to (i) reliably and timely detect items leaving the shopping floor area and (ii) assign them to individual customers. We demonstrate the system’s feasibility using a large data set collected in the laboratory under real-world conditions.