The goal of this research is to find out whether it is possible to use a specific type of machine learning algorithm to create new data that resembles data in a given dataset. The algorithm that is used is a variational autoencoder (VAE), this is a machine learning algorithm based around data compression and decompression. The dataset is a set of roughly 47,000 images of colorful fabric pattern designs created bya company called Vlisco. The working of a VAE is explained and implementations are discussed. Samples of generated images are shown and their quality is discussed.Eventually the results are compared to the results of a different research that focuses on the same problem but with a general adversarial network (GAN). If such algorithms work well they could potentially provide designers with new designs or inspiration quickly.