Body condition is an important trait in dairy cow management, mainly because it reflects the level and the use of body reserves and indirectly reproductive and health performance. Body condition score (BCS), which is done visually or by palpation, is the usual method on farm but is subjective and not very sensitive. The aim was here to develop and to validate 3DBCS which estimates BCS from 3D-shapes of dairy cows rear, the body area commonly used to assess BCS. For the calibration, a set of 57 3D-shapes from 56 Holstein cows with large BCS variability (0.5 to 4.75 on a 0–5 scale) were transformed with a principal component analysis (PCA). A multiple linear regression was fitted on the principal components to assess BCS. Four anatomical landmarks were extracted to normalize the 3D-shapes: the validation results of a manual labeling proved the concept. Then an automated labeling method was developed to extract them. Prior to the PCA, the 3D-shapes were either regularized to fill in the holes or not regularized. External validation was evaluated on 2 sets: one with cows used for calibration, but with a different lactation stage (valididem) and one with cows not used for calibration (validdiff). Repeatability was estimated with 6 cows scanned 8 times each the same day. The automated method performed slightly better than manual method for external validation (RMSE = 0.27 versus 0.34 for validdiff) and both were more repeatable than usual BCS (σ = 0.20 for 3DBCS and 0.28 for BCS). Surprisingly, regularizing the 3D-shapes performed slightly less than without regularization. Nevertheless regularization should be an interesting process before BCS assessing, especially to avoid discarding too many 3D-shapes. The first results of 3D-BCS monitoring in dairy cows with a fully automated method show promising results in terms of phenotyping. The next step will try to reduce scanning time to decrease the number of bad 3D-shapes due to cow’s movement without losing too much resolution.