Abstract: This paper presents a set of tools and techniques originating in the framework of artificial neural networks, that can be used to investigate parameters for simulations of mathematical models in systems biology. The central tool is ensemble of multilayer perceptrons used as a classifier that generates a prediction on the outcome of a simulation run with a given set of parameters. A model for a gene regulatory network in the shoot apical meristem of arabidopsis thaliana is used to demonstrate possible difficulties for the training of the classifier that can originate in the model itself as well as in the algorithm used to search parameter space for good configurations. A set of solutions for these difficulties is presented and tested on data from the simulation, and it is shown that these methods are sufficient to extract information about the model and to give an accurate prediction if certain conditions are fullfilled.