Automated visual inspection is defined as a quality control task that determines automatically if a product, or test object, deviates from a given set of specifications using visual data. In the last 25 years, 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, automated visual inspection systems still suffer from i) detection accuracy, because there is a fundamental trade off between false alarms and miss detections; and ii) strong bottleneck derived from mechanical speed and from high computational cost. For this reasons, automated visual inspection remains an open question. In this sense, Automated Multiple View Inspection, a robust method that uses redundant views of the test object to perform the inspection task, is opening up new possibilities in inspection field by taking into account the useful information about the correspondence between the different views. This strategy 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 methodology and showing experimental results obtained on real data.