Background: Use of statistical modelling techniques to identify models that both describe glaucomatous sensitivity decay and allow predictions of future field status. Method: Twelve initially normal fellow eyes of untreated patients with confirmed normal-tension glaucoma were studied. All had in excess of 15 Humphrey fields (mean follow-up 5.7 years). From this cohort individual field locations were selected for analysis if they demonstrated unequivocal deterioration at the final two fields. Forty-seven locations from five eyes satisfied this criterion and were analysed using curve-fitting software which automatically applies 221 different models to sensitivity (y) against time of follow up (x). Curve-fitting was then repeated on the first five fields, followed by projection to the date of the final field to generate a predicted threshold which was compared to the actual threshold. Competing models were therefore assessed on their performance at adequately fitting the data (R ²) and their potential to predict future field status. Results: Models that provided the best fit to the data were all complex polynomial expressions (median R ² 0.93). Other simple expressions fitted fewer locations and exhibited lower R ² values. However, accuracy in predicting future deterioration was superior with these less complex models. In this group a linear expression demonstrated an adequate fit to the majority of the data and generated the most accurate predictions of future field status. Conclusions: A linear model of the pointwise sensitivity values against time of follow-up can provide a framework for detecting and forecasting glaucomatous field progression. Linear modelling allows the clinically important rate of sensitivity loss to be estimated.