A model to predict acidotic risk of barley grain was developed from a batch culture consisting of 50 barley samples varying in bulk density (BD), processing method (standard vs. precision processing: sieving grains into large versus small kernels and rolling based on kernel size), processing index (BD after rolling/BD before rolling), processing (ground vs. dry-rolled), geographical and agronomic origin. The objective of this study was to develop a model that can predict the relative ‘hotness’ of individual barley samples. Of all the independent variables (pH, starch content, dry matter disappearance (DMD), neutral detergent fiber, acid detergent fiber, in vitro gas kinetics, total and molar proportions of individual short-chain fatty acids at different incubation times) considered, dry matter disappearance at 6 h of incubation (DMD6) accounted for 90.5% of the variation in acidosis index with a root mean square error (RMSE) of 4.46%. When the new model (−0.7826 + 2.5536 × DMD6) was applied to 3 independent data sets to predict acidosis, it accounted for 33.4, 90.9 and 25.6% of the variation in calculated acidosis index. Significant (P < 0.01) mean bias was evident in 2 of the data sets and it under-predicted acidosis index by 26.1 and 5.35%. There were marked similarities in the acidosis index ranking of barley samples by the models as shown by the result of a correlation analysis between calculated and predicted acidosis index (R² = 0.67, P < 0.01). We observed variations in the acidosis index ranking of samples that were processed differently (processing index of 75 versus 85% and precision processing versus control). Results suggest that our model which is based on DMD6 has the potential to predict acidosis risk and can rank different barley samples based on their acidotic risk; however, the model would benefit from further refinement.