This study investigated the capabilities of artificial neural networks to identify spontaneous and pressure support ventilation modes from gas flow and airway pressure signals. After receiving written informed consent, flow and pressure waveforms were recorded from 13 patients undergoing general anesthesia. During analysis, the inspiratory phase of each breath was extracted and normalized in amplitude and wavelength. Neural networks were configured to input flow, pressure, or both waveforms and to output the ventilatory mode. Neural network training was accomplished with data from 500 breaths obtained from 7 patients. Neural network performance was tested with 433 breaths from the remaining 6 patients. Networks using flow, pressure, and both waveforms recognized correctly 78% (337), 97% (423), and 100% (433) of the test waveforms, respectively. Results indicate that neural networks can be used effectively for breathing pattern recognition and encourage the application of neural networks in other types of respiratory pattern recognition problems.