The National Park Service Road Inventory Program (RIP) uses automated collection and processing of pavement-condition data. The study examines various approaches to quality assurance (QA) sampling used to evaluate those data. The RIP includes automated collection and processing of pavement-condition and roadside-inventory-feature data at 258 national parks, covering over 5,000 mi of paved roads. Evaluating the quality of some data elements requires manual data assessment conducted by trained data analysts. Because of the high volume of the collected and processed data, it is economically impractical to check quality of all data manually. To overcome this difficulty, a search for an appropriate QA data sampling methodology was conducted. The goal of the investigation was to determine appropriate statistical procedures and required sample sizes so that conclusions based on QA sample testing could be extrapolated to the whole data set with a certain level of confidence. Evaluation of QA sampling approaches, including the selection of statistical procedures for QA testing, determination of QA sample sizes, and development of the procedures for evaluating the QA testing results applicable to the RIP, is presented. The results will be of interest to practitioners involved in automated collection of pavement data and to researchers involved in the design of statistical testing procedures for engineering QA applications.