In the present study, 500 steers were used to develop models for predicting the percentage of intramuscular fat (PIMF) in live beef cattle. Before slaughter, steers were scanned across the 11th and 13th ribs using Aloka 500V (AL-500) and Classic Scanner 200 (CS-200) machines. Four to five images were collected per individual steer using each machine. After slaughter, a cross-sectional slice of the longissimus muscle from the 12th rib facing was used for chemical extraction to determine actual carcass percentage of intramuscular fat (CPIMF). Texture analysis software was used by two interpreters to select a region for determination of image parameters, which included Fourier, gradient, histogram, and co-occurrence parameters. Four prediction models were developed separately for each of AL-500 and CS-200 based on images captured by the respective machines. These included models developed without transformation of CPIMF (Model I), models based on logarithmic transformation of CPIMF (Model II), ridge regression procedure (Model III), and principal component regression procedure (Model IV). Model R2 and root mean square error of AL-500 Models I, II, III, and IV were 0.72, 0.84%; 0.72, 0.85%; 0.69, 0.91%; and 0.71, 0.86%; respectively. The corresponding R2 and root mean square error values of CS-200 Models I, II, III, and IV were 0.68, 0.87%; 0.70, 0.85%; 0.64, 0.94%; and 0.65, 0.91%; respectively. Initially, AL-500 and CS-200 prediction models were validated separately on an independent data set from 71 feedlot steers. The overall mean bias, standard error of prediction, and rank correlation coefficient across the four AL-500 models were 0.42%, 0.84%, and 0.88, respectively. For the four CS-200 models, the corresponding overall mean values were 0.67%, 0.81%, and 0.91, respectively. In a second validation test, only Model II of AL-500 and CS-200 was evaluated separately based on data from 24 feedlot steers. The overall mean bias, absolute difference, and standard error of prediction of AL-500 Model II were 0.71, 0.92, and 0.98%. For CS-200 Model II, the corresponding values were 0.59, 0.97, and 1.03%. Both AL-500 and CS-200 equipment can be used to accurately predict PIMF in live cattle. Further improvement in the accuracy of prediction equations could be achieved through increasing the development data set and the variation in PIMF of cattle used.