The confusion matrix is the standard way for reporting the accuracy of land cover and other information classified from remote-sensing imagery. This letter describes a geographically weighted method for generating spatially distributed measures of accuracy (overall, user and producer accuracies) from a logistic geographically weighted regression. A kernel-based approach defines the data and weights that are used to calculate the accuracies at each location in the study area. The results compare the global accuracy measures from a standard confusion matrix with those that have been allowed to vary locally. Maps of spatially varying user and producer accuracies describe the spatial autocorrelation of error. The use of geographically weighted models in the context of land cover accuracy is discussed and suggested as a generic approach for examining how and where error processes vary.