This study aimed to assess the ability of observers to objectively evaluate the body condition of dairy cows using a five-point scale. Four observers independently assessed 225 Holstein cows, scoring their body condition and describing seven specific body regions: the thurl region, ischial and ileal tuberosities, ilio-sacral and ischio-coccygeal ligaments, transverse processes of the lumbar vertebrae, and spinous processes of the lumbar vertebrae. An absolute body condition score (BCS) was assigned to each cow based on the modal BCS or mean score if no modal score existed. Statistical analysis using principal component analysis (PCA) revealed that four principal component vectors explained 83.6% of the variation in the body region correlation matrix. The first principal component vector accounted for 55% of the variation and was uniformly correlated with all body regions. The study found that body condition could be separated into .25-unit increments between 2.5 and 4.0, while changes below 2.5 and above 4.0 could only be distinguished by .5-unit increments. Observers agreed with the absolute score 58.1% of the time, deviating by .25 units 32.6% of the time. The results suggest that a simplified scoring system based on principal descriptors of specific body regions can effectively categorize cows into BCS classes.This study aimed to assess the ability of observers to objectively evaluate the body condition of dairy cows using a five-point scale. Four observers independently assessed 225 Holstein cows, scoring their body condition and describing seven specific body regions: the thurl region, ischial and ileal tuberosities, ilio-sacral and ischio-coccygeal ligaments, transverse processes of the lumbar vertebrae, and spinous processes of the lumbar vertebrae. An absolute body condition score (BCS) was assigned to each cow based on the modal BCS or mean score if no modal score existed. Statistical analysis using principal component analysis (PCA) revealed that four principal component vectors explained 83.6% of the variation in the body region correlation matrix. The first principal component vector accounted for 55% of the variation and was uniformly correlated with all body regions. The study found that body condition could be separated into .25-unit increments between 2.5 and 4.0, while changes below 2.5 and above 4.0 could only be distinguished by .5-unit increments. Observers agreed with the absolute score 58.1% of the time, deviating by .25 units 32.6% of the time. The results suggest that a simplified scoring system based on principal descriptors of specific body regions can effectively categorize cows into BCS classes.