Data from 400 cow carcasses were used to develop a new quality grading system. First principal component (FPC) values were determined for shear force (SHR) and palatability attributes of tenderness, connective tissue amount, flavor, and juiciness (JUC). Associated eigenvector load values for those traits were -.48, .54, .51, .46, and .05, respectively, and FPC values ranged from -3.20 to 2.18. Linear regression, using FPC as the independent variate, and SHR and palatability attributes (8-point scale, where 8 was highest) as dependent variates explained a significant amount of variation (P less than .001) in all traits, except JUC, and resulted in R2 values of .71, .89, .80, .66, and .01, respectively. A predictive quality-grade equation was developed using FPC as the dependent variate, and overall maturity (OM), lean color, marbling score (MS), lean firmness, lean texture, fat color (FC), and marbling fineness ([marbling texture + marbling distribution]/2; 8-point scale, where 8 was highest) as independent variates. The resulting best-fit prediction equation (FPC = -.052-[.0031 x OM] + [.0013 x MS] + [.31 x FC]) explained a significant amount (P less than .001) of variation in FPC with R2 = .53, Cp = 15.0, and residual standard deviation = .69. A short-cut equation was developed from this prediction equation. Simple correlations for OM, MS, and FC with FPC were -.56, .39, and .64, respectively. Cow carcasses were assigned to one of three quality grades based on FPC values that corresponded with predetermined acceptability levels for each palatability trait. Newly developed grades were quantitative and less variable than existing grades. Future grades for cow carcasses should include fat color to predict palatability.