This research focuses on the data quality control methods for evaluating the performance of Weigh-In-Motion (WIM) systems on Oregon highways. This research identifies and develops a new methodology and algorithm to explore the accuracy of each station's weight and spacing data at a corridor level, and further implements the Statistical Process Control (SPC) method, finite mixture model, axle spacing error rating method, and data flag method in published research to examine the soundness of WIM systems. This research employs the historical WIM data to analyze sensor health and compares the evaluation results of the methods. The results suggest the new triangulation method identified most possible WIM malfunctions that other methods sensed, and this method unprecedentedly monitors the process behavior with controls of time and meteorological variables. The SPC method appeared superior in differentiating between sensor noises and sensor errors or drifts, but it drew wrong conclusions when accurate WIM data reference was absent. The axle spacing error rating method cannot check the essential weight data in special cases, but reliable loop sensor evaluation results were arrived at by employing this multiple linear regression model. The results of the data flag method and the finite mixed model results were not accurate, thus they could be used as additional tools to complement the data quality evaluation results. Overall, these data quality analysis results are the valuable sources for examining the early detection of system malfunctions, sensor drift, etc., and allow the WIM operators to correct the situation on time before large amounts of measurement are lost.