In this report on our final project for CBE448/MAT481, we study the identification of nonlinear systems in a specific setting using artificial neural networks. Specifically, we train an artificial neural network on generated time-series data from a three-variable autocatalator and attempt to predict (t + N) timesteps ahead from the original data. We study the bifurcations of both our time-series data and find contrary to our expectations that the bifurcations of the two are disparate.