In order to simulate an entire operation during inhalational anaesthesia, a patient model that includes four inputs, which are the patient's age, weight, gender and anaesthetic agent concentration, and four outputs, which are the heart rate (HR), systolic arterial pressure (SAP), end-tidal anesthetic agents (Etaa), and electroencephalograph (EEG) signals (i.e., the bispectral index), was constructed in this study using artificial neural networks (ANN). The assessment of the performance of patient model presented here is based not only on the minimum cost function, but also on pharmacological reasoning. Eight patients were trained using the weighting function of ANN. After the weights were finalized, eleven more patients were tested and compared with the previous eight patients. The average cost function for the training patients (i.e., 8 patients) and testing patients (i.e., 11 patients) was 0.0834 +/- 0.0577 and 0.0563 +/- 0.0450, respectively. The results show that this approach can provide a more robust model despite the considerable individual variation in inhalational anaesthesia among patients.