In this research we investigate the relationship between multifactor investing and Artificial Neural Network (ANN) and contribute to modern stock market prediction. We present the components for multifactor investing i.e. value, quality, size, low volatility & momentum as well as a methodology for ANN which provides the theory for the results. The return for the multifactor funds tested in this research is recorded below the benchmark used. However, the factors do have a dynamic relationship when testing for correlation and the multifactor regression analysis showed a high explanatory power (R2) for the funds. Based on the methodology of an ANN we establish that it is possible to use the knowledge from multifactor investing to train the technology with. When summarizing peer reviewed journals, we find that momentum have already been recurrently used in previous stock market prediction systems based on ANN, but the remaining factors have not. We conclude that there is an opportunity to use several factors to train an ANN due to their dynamic relationship and unique characteristics.