Milk yield is a fundamental observation in many experiments and is commonly determined using integrated milk meters that determine milk weight as the cow is being milked. These meters are used heavily in a harsh environment and commonly are not calibrated regularly, so mechanical or other problems may create variation in milk weight data. Additionally, direct calibration by collection of milk in a bucket is difficult and imperfect as use of the bucket may change flow rates. The objective of this work was to define a method to easily check parlor meter calibration and adjust milk weight values for variation between individual stalls in a parlor. Because most cows are milked in a different stall at each milking, Dr. L. Armentano (University of Wisconsin) originally proposed that an effect of stall could be determined that would represent the calibration of the stall. The effect of stall on milk yield was modeled using 7 d of data from approximately 200 cows using a mixed model that included the fixed effect of stall and the random effects of day, milking (AM/PM), and cow. Stalls requiring service can be identified based on the stall deviations, which can exceed 2 kg in malfunctioning stalls. A correction factor for each stall can be generated by dividing the least squared mean of the stall by the overall mean. Milk yields can then be corrected by multiplying the meter weight value by the correction factor. The method was used in multiple experiments with 250 to 1500 milk yield observations. In all data sets, stall correction of milk weights slightly improved model fit and decreased standard errors, indicating reduced variation. Second, raw meter and corrected values were compared with weight of milk collected in a bucket (n = 3 per stall). The corrected values had a 4% greater R² than uncorrected values. Modeling stall deviations in milk meters is a simple, convenient, and low cost method to monitor milk meter functionality and accuracy and can be used to reduce data variation and experimental error.