Childhood obstructive sleep apnea-hypopnea syndrome (OSAHS) is a highly prevalent condition that negatively affects health, performance and quality of life of infants and young children. Early detection and treatment improves neuropsychological and cognitive deficits linked with the disease. The aim of this study was to assess the performance of automated analysis of blood oxygen saturation (SpO2) recordings as a screening tool for OSAHS. As an initial step, statistical, spectral and nonlinear features were estimated to compose an initial feature set. Then, fast correlation-based filter (FCBF) was applied to search for the optimum subset. Finally, the discrimination power (OSAHS negative vs. OSAHS positive) of three pattern recognition algorithms was assessed: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA) and logistic regression (LR). Three clinical cutoff points commonly used in the literature for positive diagnosis of the disease were applied: apnea-hypopnea index (AHI) of 1, 3 and 5 events per hour (e/h). Our methodology reached 88.6% accuracy (71.4% sensitivity and 100.0% specificity, 100.0% positive predictive value, and 84.0% negative predictive value) in an independent test set using QDA for a clinical cut-off point of 5 e/h. These results suggest that SpO2 nocturnal recordings may be used to develop a reliable and efficient screening tool for childhood OSAHS.