E-commerce has grown at a rapid pace over the last years. At the same time, the return rate of purchased products is high, causing unnecessary transportation of goods to the home of the customer and back. In the clothing industry, most of the returns are related to the size of theproduct. Therefore, a growing demand for digital tools that can assist the customer in finding the correct size before ordering the product, thus avoiding a potential size related return. This thesis applies machine learning to the problem to recommend the correct size of the product to the customer before the purchase is made. Especially, focusing on the stakeholder’s three dimensional size system for trousers by evaluating four different machine learning approaches. Results show increased accuracy compared to the benchmark, yet provide no clear indication ofa specific machine learning approach as favorable using the data sets provided by the stakeholder. Several shortcomings of the data sets with regard to increasing the accuracy are proposed and discussed as potential problems causing noise and confusion into the machine learning models.