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Context Measurement of plasma steroids is necessary for diagnosis of congenital adrenal hyperplasia (CAH). We sought to establish an efficient strategy for detection and subtyping of CAH with a machine-learning algorithm. Methods Clinical phenotype and genetic testing were used to provide CAH diagnosis and subtype. We profiled 13 major steroid hormones by liquid chromatography–tandem mass spectrometry (LC-MS/MS). A multi-classifier system was established to distinguish 11β-hydroxylase deficiency (11βOHD), 17α-hydroxylase/17,20-lyase deficiency (17OHD) and 21α-hydroxylase deficiency (21OHD) in a discovery cohort (N=226). It was then validated in an independent cohort (N=111) and finally applied in a perspective cohort of 256 patients. The diagnostic performance on the basis of area under receiver operating characteristic curves was evaluated. Results A cascade logistic regression model, we named the “Steroidogenesis Score”, was able to discriminate the three most common CAH subtypes: 11βOHD, 17OHD, 21OHD. In the perspective application cohort, the Steroidogenesis Score had a high diagnostic accuracy for all three subtypes, 11βOHD (AUC, 0.994; 95% CI, 0.983-1.000), 17OHD (AUC, 0.993; 95% CI, 0.985-1.000) and 21OHD (AUC, 0.979; 95% CI, 0.964-0.994). For nonclassic 21OHD patients, the tool presented with significantly higher sensitivity compared with measurement of basal 17α-hydroxyprogesterone (17OHP) (0.973 vs 0.840, p=0.005) and was not inferior to measurement of basal versus stimulated 17OHP (0.973 vs 0.947, p=0.681). Conclusions The Steroidogenesis Score was biochemically interpretable and showed high accuracy in identifying CAH patients, especially for nonclassic 21OHD patients, thus offering a standardized approach to diagnose and subtype CAH.