Principal component analysis (PCA) is a vital statistical technique for defining the morphological structure of livestock but has not been used in South African Kalahari Red goats. Thirteen morphometric traits and eleven body indices from two hundred and ninety-six (296) South African Kalahari Red goats (269 does and 27 bucks) aged 2–3 years were used to define morphological structure using PCA. The coefficient of determination (R2), root mean square error (RMSE), Akaike’s information criterion (AIC), Mallows' Cp-statistic (Cp), and coefficient of variation (CV) were used to select the best fit model. Body weight was correlated with all morphometric traits in both sexes. The first two principal components explained 87.31% of the variation in measurements from male goats and 62.32% of the trait variation in the females. The inclusion of head length, body length, canon circumference, rump length, rump width, body condition score, wither height, and rump height increased the accuracy to 98% with smaller RMSE (2.42), AIC (55.35), Cp (10.00), and CV (3.98), and the use of PC1 and PC2 included 94% of the variation (RMSE, 3.62; AIC, 72.26; Cp, 3.00; CV, 5.94 in males). In females, the inclusion of all morphometric traits included 87% of the variation (RMSE, 2.93; AIC, 590.63; Cp, 13.00; CV 5.87). The use of PC1 and PC2 included 82% of the variation (RMSE, 3.41; AIC, 663.60; Cp, 3.00; CV, 6.84). PCA can therefore be used in breeding programs to define the morphological structure of South African Kalahari Red goats with a severe reduction in the number of morphometric traits to be recorded.