Visual soil evaluation (VSE) is a simple and fast method to assess soil quality in situ, and is becoming increasingly popular. Besides soil structure assessment, also other soil properties can be assessed such as grass cover, roots and earthworms. Yet, the full set of visual observations has not been properly evaluated for reproducibility and correlation with standard field or laboratory measurements, for several soil types. The objectives of this study were therefore to evaluate the reproducibility and the correlation of visual observations with closely related field or laboratory measurements. We used quantitative visual observations where possible, to enhance objectivity of VSE. The reproducibility and correlation of visual observations with standard measurements was evaluated for three soil types (sand, peat and clay) in the North Friesian Woodlands, The Netherlands. Reproducibility of quantitative visual observations was tested by comparing observations made by farmers and soil scientists, on the same soils. A linear mixed-effect model indicated that for all quantitative visual observations except for the depth of soil compaction, subjectivity due to the observers’ background (farmer or soil scientist) had no significant effect on the observations. For assessment of relative soil quality differences between sites, the results suggested that a single observer can make the visual observations, when assessing the fraction largest soil structural elements, earthworms, gley mottles and the depth of soil compaction. Spearman's rank correlation coefficients indicated that visual observations of grass cover, root count, maximum rooting depth and the fraction largest soil structural elements correlated significantly with closely related field or laboratory measurements regardless of soil type. Maximum rooting depth, root count, soil colour, the fraction largest soil structural elements, and the degree of soil compaction only significantly correlated with field or laboratory measurements for specific soil types. Analyses showed that the correlation of visual observations with standard measurements were soil type dependent, suggesting that the evaluation of soil quality should also be soil type dependent.