Title: Interpretable Out-of-Distribution Detection using Pattern Identification. Abstract: Out-of-distribution (OoD) detection for data-based programs is a goal of paramount importance. Common approaches in the literature tend to train binary classifiers requiring inside-of-distribution (IoD) and OoD validation samples, and/or implement confidence metrics that are often abstract and therefore difficult to interpret. In this work, we propose to use the PARTICUL pattern identification algorithm in order to build more interpretable and robust OoD detectors for visual classifiers. Crucially, this approach does not require retraining the classifier and is tuned directly to the IoD dataset, making it applicable to domains where OoD does not have a clear definition. Moreover, pattern identification allows us to provide images from the IoD dataset as reference points to better explain our confidence scores. We illustrate the generalization abilities of our approach through an extensive benchmark across four datasets and two definitions of OoD. Our experiments show that the robustness of all metrics under test does not solely depend on the nature of the IoD dataset or the OoD definition, but also on the architecture of the classifier, which stresses the need for thorough experimentations for future work in OoD detection.