This report describes an approach of creating a system identifying fruit and vegetables in the retail market using images captured with a video camera at- tached to the system. The system helps the customers to label desired fruits and vegetables with a price according to its weight. The purpose of the sys- tem is to minimize the number of human computer interactions, speed up the identification process and improve the usability of the graphical user interface compared to existing systems. To accomplish creating a system improving these properties, an idea of implementing machine learning to identify the products aroused. Instead of assigning the responsibility to the user, who usually iden- tify the products manually, the responsibility is given to a computer. To classify an object, different convolutional neural networks have been tested and retrained. The networks have been retrained on data sets collected from ImageNet. To improve the accuracy, the networks have also been retrained on images where the background environment is similar to the environment the networks are supposed to perform in. The networks tested in this report are MobileNet and Inception. The networks have different propagation time and varies in accuracy. MobileNet performs the classification about seven times faster than Inception, but Inception gives more accurate results. To improve the systems further, usability testing has been performed on the graphical user interface of existing system and resulted system. To test the usability, a heuristic evaluation has been performed in combination of a second test produced by the authors. The tests concluded that the resulted system was more user friendly compared to existing systems. The hardware of the system constitutes of a Raspberry Pi, camera, display, load cell and a case. The software includes Python-code to label an image, a graphical user interface to interact with the user and a server created with Node.js. The graphical user interface has been programmed with JavaScript supplemented with the React library. To conclude, implementing convolutional neural networks to classify images and developing a new user interface resulted in a faster identification process together with fewer usability flaws.