Interpretation of numeric automated threshold visual field results is often difficult. A large amount of data is obtained for every single field tested. Various approaches to summarize this data have been suggested, most commonly the mean and standard deviation of departures from age-corrected normal threshold values. These visual field indices differ substantially from subjective field interpretation where spatial relationships are important. We have previously devised two methods for automated field interpretation which take spatial information into account--regional up-down comparisons and arcuate cluster analysis. We now studied the merits of using these new spatial methods and compared them to traditional visual field indices for discrimination between normal and glaucomatous field results. Central static 30 degree field results in 101 eyes of 101 normal subjects and 101 eyes of 101 patients with glaucoma were discriminated using logistic regression analysis. The best field classification was obtained with a spatial visual field model combining up-down differences and arcuate clusters. The advantages of the spatial model were confirmed in an independent material of 163 eyes of 163 normal subjects and 76 eyes of 76 patients with glaucoma where eyes with large field defects had been removed. In this material the spatial model gave 87% sensitivity and 83% specificity while the best non-spatial model gave 82% sensitivity and 80% specificity. Visual field interpretation in glaucoma may be significantly enhanced if detection is focused on circumscribed field loss rather than on averages of differential light sensitivities and similar indices which do not take spatial relationships into consideration.