Ensuring the safe consumption of suitable food products is a top priority for food companies. There are several standard prevention systems that identify and eliminate contaminated products such as x-rays or infrared systems. Such systems are not reliable enough as they cannot detect low density materials, have low depth of penetration and x-rays can be harmful to operators. An emerging technique in the food world is Microwave Imaging Technology, which allows to overcome these problems and can detect low density materials. In this thesis, a Machine Learning algorithm is designed and implemented on a Microwave Sensing system that is able to detect foreign bodies in jars of cocoa-hazelnut spreadable cream during their passage on a conveyor belt at a speed of 30 and 50 cm/s.