Neural network architectures can be regularised by adding a penalty term to the objective function, thus minimising network complexity in addition to the error. However, adding a term to the objective function inevitably changes the surface of the objective function. This study investigates the landscape changes induced by the weight elimination penalty function under various parameter settings. Fitness landscape metrics are used to quantify and visualise the induced landscape changes, as well as to propose sensible ranges for the regularisation parameters. Fitness landscape metrics are shown to be a viable tool for neural network objective function landscape analysis and visualisation.