Attempts to identify physiological control systems using traditional engineering or statistical approaches generally fails to produce generalized models. This report presents an alternative approach using a hybrid model. An artificial neural network is used to model the control system and a lumped parameter model is used to model the passive system. Two artificial (model) water bath systems and two robot arm models were developed to test the feasibility of this approach. Observed data were generated using these model systems by recording responses to simulated perturbations. The hybrid model was then fit to the observed data by adjusting neural network connection weights to minimize the error between observed and predicted values for one or more system variables. The fitting procedure was repeated three times under each set of conditions. Only 2-3 hidden layer nodes were required to simulate the artificial model systems. It was not necessary to include the control system output in the error term. The methodology was robust; successful control system identification was performed when errors (+/- 10% variance) were introduced into the observed data set and also when errors (+/- 10% variance) were introduced into the passive system parameter values.