&NA;A validation study has been performed to determine the error detection capabilities of a new quality control (QC) technology called “intelligent Quality Management (iQM).” iQM is a completely automated statistical QC process that uses frequent measurements of internal process control solutions to monitor measurement variation and signal abnormal drifts, then applies pattern recognition algorithms to identify the type of error and trigger appropriate corrective actions. The validation methodology follows National Committee for Clinical Laboratory Standards (NCCLS) and International Standards Organization (ISO) guidelines and involves characterizing method performance on the sigma‐scale, characterizing instrument drift limits as statistical control rules, then assessing the probabilities of rejecting runs whose errors would exceed defined quality requirements, such as the Clinical Laboratory Improvement Amendment (CLIA) proficiency criteria for acceptable performance. Practical measures of performance are obtained by determining the average run lengths and converting them to the average times for detecting errors. With iQM, medically important errors will be detected within 0.05 to 0.5 hours for pH, PCO2, PO2, potassium, calcium, lactate, and hematocrit; 0.12 to 1.2 hours for glucose; and 0.17 to 1.7 hours for sodium. Compared to current QC practices where controls are analyzed every 8 to 24 hours, the iQM technology is expected to provide faster detection of errors.