In recent years several aids for automated interpretation of visual field data have been suggested. We believed that incorporation of thorough knowledge of normal visual field variability would allow improvements in the performance of such aids since more attention would be paid to field results in areas with low physiological variability. Two visual field models for classification of fields in glaucoma based on comparisons of sensitivity values in the upper and lower hemifields and on analysis of test point clusters with diminished sensitivity were compared. Both models were constructed using logistic regression analysis in 101 normal eyes and 101 eyes with glaucoma. The first, more traditional model assumed Gaussian distributions of deviations from age-corrected normal thresholds and constant variability across the field (non-weighted model). The second model took into account empirically determined variability of pointwise threshold results and of cluster volumes in various visual field regions (weighted model). The two models were subsequently tested on an independent material of 163 normal eyes and 76 eyes with glaucoma. The weighted model gave significantly better classification of the fields in both materials. Accounting for physiological threshold variability can offer significant advantages in the construction of perimetric analysis aids for detection of glaucoma.