This paper illustrates the use of combined neural network models to guide model selection for diagnosis of internal carotid arterial disorders. The method presented in this study was directly based on the consideration that internal carotid arterial Doppler signals are chaotic signals. This consideration was tested successfully using the nonlinear dynamics tools, like the computation of Lyapunov exponents. Statistics were used over the set of the Lyapunov exponents in order to reduce the dimensionality of the extracted feature vectors. The first level networks were implemented for the diagnosis of internal carotid arterial disorders using the selected Lyapunov exponents as inputs. To improve diagnostic accuracy, the second level network was trained using the outputs of the first level networks as input data. The combined neural network models achieved accuracy rates which were higher than that of the stand-alone neural network models.