For some time now, the fashion industry has been a big contributor to humanity's carbon emissions. If we are to become a more sustainable society and cut down on our pollution, this industry needs to be reformed. The clothes we wear must be reused to a greater extent than today. Unfortunately, a big part of the Swedish population experiences a lack of available items on the second-hand market. This paper presents a proof-of-concept application that could be a possible solution. The application scans online second-hand websites and separates composite ads into new, separate, ads. This makes it easier for potential buyers to find the items they are looking for. The application uses a web scraper written in Java combined with a convolutional neural network for classification. The CNN is a modified version of the ResNet50 model which is trained on a dataset collected from a Swedish second-hand site. At the moment the network supports 5 types of clothing with an accuracy of 86%. Tests were performed to investigate the potential of scaling up the model. These experiments were made using a 3rd party dataset called deepFashion. This dataset consists of over 800,000 images of clothes in different settings. The tests indicate that given a larger dataset the model could handle up to 31 classes with an accuracy of at least 57% and possibly as high as 76%. This evolved model did not produce any meaning full results when tested on real second-hand images since the deepFashion network mostly consists of clothes worn by models. Further research could see this application evolve into one that could sort ads on not only type, but colour, material and other properties to provide even more exhaustive labels.