A method for monitoring respiratory support for a patient having an airway, wherein said method comprises: providing a monitoring system comprising: (a) at least two sensors adapted to monitor the patient, or to monitor a patient airway access, each sensor generating an output signal, said output signals comprising at least a plethysmography output signal and an exhaled carbon dioxide (ExCO2) output signal; (b) an operator interface that generates at least one operator input signal, and (c) a processing subsystem adapted to receive the at least two of the output signals and at least one operator input signal, wherein the processing subsystem has a processor and a memory and is adapted to run under control of a program stored in the memory, wherein the processing subsystem evaluates at least two output signals and at least one operator input signal to determine a desired setting for at least one ventilation parameter and wherein the processing system comprises a neural network; receiving into the processing subsystem at least two of the output signals; implementing the processing subsystem to evaluate the at least two output signals and at least one operator input signal to assess respiratory support provided to the patient; and providing a recommendation by the monitoring system for the desired setting for at least one ventilation parameter based on the evaluation of the at least two output signals and at least one operator input signal by the processing subsystem, characterized in that the plethysmography output signal and the exhaled carbon dioxide (ExCO2) output signal are used by the neural network to track changes in oxygenation and the effect of PEEP on the cardiac system and thereby to recommend a desired setting for a positive end expiratory pressure (PEEP) signal to optimize oxygenation without sacrificing cardiac output. providing a monitoring system comprising: (a) at least two sensors adapted to monitor the patient, or to monitor a patient airway access, each sensor generating an output signal, said output signals comprising at least a plethysmography output signal and an exhaled carbon dioxide (ExCO2) output signal; (b) an operator interface that generates at least one operator input signal, and (c) a processing subsystem adapted to receive the at least two of the output signals and at least one operator input signal, wherein the processing subsystem has a processor and a memory and is adapted to run under control of a program stored in the memory, wherein the processing subsystem evaluates at least two output signals and at least one operator input signal to determine a desired setting for at least one ventilation parameter and wherein the processing system comprises a neural network; (a) at least two sensors adapted to monitor the patient, or to monitor a patient airway access, each sensor generating an output signal, said output signals comprising at least a plethysmography output signal and an exhaled carbon dioxide (ExCO2) output signal; (b) an operator interface that generates at least one operator input signal, and (c) a processing subsystem adapted to receive the at least two of the output signals and at least one operator input signal, wherein the processing subsystem has a processor and a memory and is adapted to run under control of a program stored in the memory, wherein the processing subsystem evaluates at least two output signals and at least one operator input signal to determine a desired setting for at least one ventilation parameter and wherein the processing system comprises a neural network; receiving into the processing subsystem at least two of the output signals; implementing the processing subsystem to evaluate the at least two output signals and at least one operator input signal to assess respiratory support provided to the patient; and providing a recommendation by the monitoring system for the desired setting for at least one ventilation parameter based on the evaluation of the at least two output signals and at least one operator input signal by the processing subsystem, characterized in that the plethysmography output signal and the exhaled carbon dioxide (ExCO2) output signal are used by the neural network to track changes in oxygenation and the effect of PEEP on the cardiac system and thereby to recommend a desired setting for a positive end expiratory pressure (PEEP) signal to optimize oxygenation without sacrificing cardiac output.