Automated visual inspection of metal castings is defined as a quality control task that determines automatically if a casting deviates from a given set of specifications using visual data. Many research directions in this field have been exploited, some very different principles have been adopted and a wide variety of algorithms have been appeared in the literature. However, the developed approaches are tailored to the inspection task, i.e., there is no common approach applicable to all cases because the development is an ad hoc process. Additionally, detection accuracy should be improved, because there is a fundamental trade off between false alarms and miss detections. For these reasons, we proposed a novel methodology, called Automated Multiple View Inspection, that uses redundant views of the test object to perform the inspection task. The method is opening up new possibilities in inspection field by taking into account the useful information about the correspondence between the different views. It is very robust because in first step it identifies potential defects in each view and in second step it finds correspondences between potential defects, and only those that are matched in different views are detected as real defects. In this paper, we review the advances done in this field giving an overview of the multiple view inspection and showing experimental results obtained on metal castings.