Meals are clusters of feedbunk visit (BV) events that are differentiated from the next meal by a nonfeeding interval that is longer compared with the nonfeeding intervals within a meal. The longest nonfeeding interval considered to be part of a meal is defined as the meal criterion. The objective of this study was to determine which combination of 2 probability density functions [(PDF): Gaussian normal (G), Weibull (W), Log-Normal, Gamma, and Gumbel] used in a bimodal distribution model had the best fit of nonfeeding interval data collected in beef heifers. Feeding behavior traits (572,627 total BV events) were measured in 119 heifers fed a high-grain diet (3.08 Mcal ME/kg DM), using a GrowSafe system for 66 d. The frequency and duration of BV events averaged 75 ± 15 events/d and 73.0 ± 22.3 min/d, respectively. The bimodal PDF combinations were fitted to the log(10)-transformed interval lengths between BV events for each animal, using R mixdist package (2.13). The Akaike Information Criterion (AIC) was used to assess goodness of fit of the 25 bimodal PDF combinations. The PDF model with the least AIC value was selected as the best fit for each individual. A χ(2) analysis of the selected best PDF distribution across individuals revealed that 78.2% of the heifers best fit were G-W or W-W PDF models. The likelihood probability estimates were calculated from the average AIC deviation of each model from the standard G-G model. The G-W likelihood probability estimate was greater (P = 0.001) than the W-W combination (0.997 vs. 0.727). Our analysis indicated the G-W model had a statistically better fit and is most likely the best approach to define meal criterion in beef heifers fed high-grain diets.