Objectives: The adverse health outcomes of being overweight and obese are overwhelming with more implications while the effects of being underweight have received little consideration. Quantile Regression (QR) was used to determine the effect of factors associated with the Body Mass Index (BMI) of women. Methods: A total of 31,828 women in their reproductive age (15–49 years) from the Nigeria Demographic and Health Survey (NDHS) 2013 data, were included in the study. Quantile regression model was fitted into the data to determine the factors related to the BMI of women Results: Women had an average age of 29 years (SD=7 years); about half of the women (49.2%) had no formal education and nearly all were married (96.0%). Quantile regression models were fitted into the data. At the 10th quantile, children ever born (β=0.17, S.E = 0.11; 95% CI: -0.039, 0.387) and place of residence (β=0.06, S.E = 0.08; 95% CI: -0.094, 0.204) did not predict BMI while family size (β=0.16, S.E = 0.07; 95% CI: 0.032, 0.295) contributed significantly to the BMI effect produced. The Pseudo R2 increased with increasing quantile and among all the quantiles, the 90th quantile model (Pseudo R2 = 0.15) better predicts the BMI distribution. Conclusion: At all quantiles; age, level of education and wealth quintiles were determinants of BMI among women of reproductive age. Quantile regression was able to detect the magnitude of the changes depending on the location of the woman in the BMI distribution.