Neurally adjusted ventilatory assist (NAVA) delivers airway pressure (P aw ) in proportion to the electrical activity of the diaphragm (EAdi) using an adjustable proportionality constant (NAVA level, cm·H 2 O/μV). During systematic increases in the NAVA level, feedback-controlled down-regulation of the EAdi results in a characteristic two-phased response in P aw and tidal volume (Vt). The transition from the 1st to the 2nd response phase allows identification of adequate unloading of the respiratory muscles with NAVA (NAVA AL ). We aimed to develop and validate a mathematical algorithm to identify NAVA AL . Paw , Vt, and EAdi were recorded while systematically increasing the NAVA level in 19 adult patients. In a multistep approach, inspiratory P aw peaks were first identified by dividing the EAdi into inspiratory portions using Gaussian mixture modeling. Two polynomials were then fitted onto the curves of both P aw peaks and Vt. The beginning of the P aw and Vt plateaus, and thus NAVA AL , was identified at the minimum of squared polynomial derivative and polynomial fitting errors. A graphical user interface was developed in the Matlab computing environment. Median NAVA AL visually estimated by 18 independent physicians was 2.7 (range 0.4 to 5.8) cm·H 2 O/μV and identified by our model was 2.6 (range 0.6 to 5.0) cm·H 2 O/μV. NAVA AL identified by our model was below the range of visually estimated NAVA AL in two instances and was above in one instance. We conclude that our model identifies NAVA AL in most instances with acceptable accuracy for application in clinical routine and research.