On-shelf availability is a measure of how available a product is for a shopper in a store. The products should be in the place where the shopper expects it and at the time the shopper wants to buy it. To ensure a high Onshelf availability store clerks must move around the store and look for products that must be replenished and products that are misplaced. However, this task is rather time consuming since there are usually hundreds of different products to keep track of in a store. This thesis therefore, aims to simplify this task for store clerks by creating an automatic system that can identify “out of stocks” and misplaced products. Different state of the art object detection algorithms and image classification methods were evaluated in order to solve this task. The object detection algorithms were used to find products and gaps on store shelves and were trained with a relatively small dataset. The bounding boxes obtained from the object detection algorithm was then forwarded to an image classification algorithm in order to predict the label of the product. The training data of the classification dataset consisted of a small dataset with 29 different detergent products. All the algorithms were evaluated on speed and F1-score while the object detection algorithms were also evaluated on an average precision with an intersection of union as 0.5 and 0.75. The results indicate that it is possible to use deep learning in order to improve on-shelf availability. However, the methods might only perform good on the small datasets used in this thesis. Furthermore, since no real test have been made in a real-life supermarket it is impossible to say for certain if it would indeed improve on-shelf availability.