An experiment was performed utilizing bioelectrical impendence technology (BIA) to predict red meat and trimmable fat yields for over-finished beef carcasses. Fifty-six single-sired steers were finished to above typical slaughter weights (603.5 kg ± 48.1 kg; 2.0 ± 0.7 cm 12th rib back fat) before harvest. After a 24-h chill period, standard grading procedures were used to derive a calculated yield grade for each animal (3.7 ± 0.9). Measures of BIA [resistance (Rs, 160.2 ± 16.5 ohms), reactance (Xc, 44.6 ± 4.8 ohms)] were quantified by introducing an alternating electrical current between positive (detector) and negative (source) electrodes placed at opposite ends of the right side of each carcass. Source electrodes introduced current through the carcass and detector electrodes detected any decrease in voltage caused by resistance to electrical current. Other measured variables included temperature (Tp, 3.1 ± 0.7°C), length between electrodes (L, 118.7 ± 5.1 cm), and hot carcass weight (HCW, 381.0 ± 32.8 kg). Impedance (I; (Rs² + Xc²)⁵; 166.3 ± 17.0) electrical volume (EVOL; L²/Rs; 89.4 ± 16.8), resistive density (RsD; RSW²/(L²/Rs); 392.4 ± 63.0), and reactive density (XcD; RSW²(L²/Xc); 108.9 ± 16.3) were derived from measured variables. Correlations were calculated between dependent and independent variables. Stepwise regression procedures were used to develop models for prediction of percentage red meat yield (RMY%) and trimmable fat yield (TFY%). Pearson correlation coefficients indicate that RMY was highly correlated (P < 0.05) to RsD (r = −0.65), XcD (r = −0.51), Tp (r = −0.46), HCW (r = −0.39), EVOL (r = 0.30), Rs (r = −0.30), and I (r = −0.28) whereas FY was correlated (P < 0.05) with RsD (r = 0.75), XcD (r = 0.60), HCW (r = 0.57) and Tp (r = 0.50). Regression models indicate that 65% and 72% of the variation in RMY% and TFY% may be attributed to BIA measures. By comparison, the calculated USDA yield grade accounted for 50.0% and 61.0% of the variation in RMY% and TFY%, respectively. These results suggest that BIA technology can be utilized as a predictor of beef carcass composition.