The actions of individual users of an experimental demand forecasting support system were traced and analyzed. Users adopted a wide variety of strategies when choosing a statistical forecasting method and deciding whether to apply a judgmental adjustment to its forecast. This was the case despite the users reporting similar levels of familiarity with statistical methods. However, the analysis also revealed that users were very consistent in the strategies that they applied across twenty different series. In general, the study found that users did not emulate mechanical forecasting systems in that they often did not choose the forecasting method that provided the best fit to past data. They also tended to examine only a small number of methods before making a selection, though they were likely to examine more methods when they perceived the series to be difficult to forecast Individuals who were relatively unsuccessful in identifying a well fitting statistical method tended to compensate for this by making large judgmental adjustments to the statistical forecasts. However, this generally led to forecasts that were less accurate than those produced by people who selected well fitting methods in the first place. These results should be of particular interest to designers of forecasting support systems who will typically have some stylised representation of the way that users employ their system to generate forecasts.