Pollution and climate change are some of the biggest challenges facing humanity. Moreover, for a sustainable future, recycling is needed. Plas- tic is a big part of the recycled material today, but there are problems that the recycling world is facing. The modern-day recycling facilities can handle plastics of all colours except black plastics. For this reason, most recycling companies have resorted to methods unaffected by colour, like the method used at Stena Nordic Recycling Central. The unawareness of the individual plastics causes the problem that Stena Nordic Recycling Central has to wait until an entire bag of plastic granulates has been run through the production line and sorted to test its purity using a chemistry method. Finding out if the electrostats divider settings are correct using this testing method is costly and causes many re-runs. If the divider set- ting is valid in an earlier state, it will save both time and the number of re-runs needed.This thesis aims to create a system that can classify different types of plas- tics by using image analysis. This thesis will explore two techniques to solve this problem. The two computer vision techniques will be the RGB method see 3.3.2 and machine learning see 3.3.4 using transfer learning with an AlexNet. The aim is the accuracy of at least 95% when classifying the plastics granulates.The Convolutional neural network used in this thesis is an AlexNet. The choice of method to further explore is decided in the method part of this thesis. The results of the computer vision method and RGB method were difficult to determine more about in section 4.2. It was not clear if one plastic was blacker than the other. This uncertainty and the fact that a Convolutional neural network takes more features than just RGB into a count, discussed in section 3.3, makes the computer vision method, Con- volutional neural network, a method to further explore in this thesis. The results gathered from the Convolutional neural network’s training was 95% accuracy in classifying the plastic granulates. A separate test is also needed to make sure the accuracy is close to the network accuracy. The result from the stand-alone test was 86.6% accurate, where the plastic- type Polystyrene had a subpar result of 73.3% and 100% accuracy when classifying Acrylonitrile butadiene styrene. The results from the Convo- lutional neural network show that black plastics could be classified using machine learning and could be an excellent solution for classifying and recycling black plastics if further research on the field is conducted.