Purpose To apply an artificial neural network (ANN) for fast and robust quantification of the oxygen extraction fraction (OEF) from a combined QSM and quantitative BOLD analysis of gradient echo data and to compare the ANN to a traditional quasi-Newton (QN) method for numerical optimization. Methods Random combinations of OEF, deoxygenated blood volume (nu), R-2, and nonblood magnetic susceptibility (chi nb) with each parameter following a Gaussian distribution that represented physiological gray matter and white matter values were used to simulate quantitative BOLD signals and QSM values. An ANN was trained with the simulated data with added Gaussian noise. The ANN was applied to multigradient echo brain data of 7 healthy subjects, and the reconstructed parameters and maps were compared to QN results using Student t test and Bland-Altman analysis. Results Intersubject means and SDs of gray matter were OEF =43.5 +/- 0.8%, R2=13.5 +/- 0.3 Hz, nu=3.4 +/- 0.1%, chi nb=-25 +/- 5 ppb for ANN; and OEF = 43.8 +/- 5.2%, R2=12.2 +/- 0.8 Hz, nu=4.2 +/- 0.6%, chi nb=-39 +/- 7 ppb for QN, with a significant difference (P<0.05) for R2, nu, and chi nb. For white matter, they were OEF = 47.5 +/- 1.1%, R2=17.1 +/- 0.4 Hz, nu=2.5 +/- 0.2%, chi nb=-38 +/- 5 ppb for ANN; and OEF =42.3 +/- 5.6%, R2=16.7 +/- 0.7 Hz, nu=2.9 +/- 0.3%, chi nb=-45 +/- 9 ppb for QN, with a significant difference (P<0.05) for OEF and nu. ANN revealed more gray-white matter contrast but less intersubject variation in OEF than QN. In contrast to QN, the ANN reconstruction did not need an additional sequence for parameter initialization and took approximately 1 s rather than roughly 1 h. Conclusion ANNs allow faster and, with regard to initialization, more robust reconstruction of OEF maps with lower intersubject variation than QN approaches.