Prepregnancy BMI is a widely used marker of maternal nutritional status that relies on maternal self-report of prepregnancy weight and height. Pregravid BMI has been associated with adverse health outcomes for the mother and infant, but the impact of BMI misclassification on measures of effect has not been quantified. The authors applied published probabilistic bias analysis methods to quantify the impact of exposure misclassification bias on well-established associations between self-reported prepregnancy BMI category and five pregnancy outcomes (small for gestational age (SGA) and large for gestational age (LGA) birth, spontaneous preterm birth (sPTB), gestational diabetes mellitus (GDM), and preeclampsia) derived from a hospital-based delivery database in Pittsburgh, PA (2003-2005; n = 18,362). The bias analysis method recreates the data that would have been observed had BMI been correctly classified, assuming given classification parameters. The point estimates derived from the bias analysis account for random error as well as systematic error caused by exposure misclassification bias and additional uncertainty contributed by classification errors. In conventional multivariable logistic regression models, underweight women were at increased risk of SGA and sPTB, and reduced risk of LGA, whereas overweight, obese, and severely obese women had elevated risks of LGA, GDM, and preeclampsia compared with normal-weight women. After applying the probabilistic bias analysis method, adjusted point estimates were attenuated, indicating the conventional estimates were biased away from the null. However, the majority of relations remained readily apparent. This analysis suggests that in this population, associations between self-reported prepregnancy BMI and pregnancy outcomes are slightly overestimated.