Invasive species can be devastating to native vegetation and can be difficult to identify unless properly trained. The best method for combating invasive species is to quickly identify and contain them. This project looks at a possible solution to difficult identification by creating a trained artificial neural network using TensorFlow that can categorize plant species by their venation patterns. This network could then be integrated into an application that identifies if a particular plant was invasive and allows for faster containment measures to be taken. For this project an artificial neural network was set up and tested using five different plant species; Acer campestre, Acer ginnala, Acer griseum, Acer platanoides, Acer negundo. Some of these species are invasive, but this project focused on categorizing species rather than if they are invasive or native. The images used for this project were pulled from the open-source Leafsnap dataset [1]. Leafsnap is a similar project that uses machine learning to categorize leaf species by leaf shape. The network was trained on a total of 135 images, 27 for each species, and tested with 35 images, 7 images for each species. After training, the network had an overall accuracy of 94% when categorizing by venation pattern. The preliminary results of this project show that with a larger dataset and a more refined artificial neural network, a reliable application can be created that will quickly identify invasive plants.