Open source GIS software GRASS releases 8.0 to 8.4 have received some long overdue improvements in imagery handling such as the ability to reuse spectral signature files of existing classifiers, machine readable output of accuracy assessment tool and a support vector machine (SVM) classifier. Practical comparison of all three pixel‐based classifiers of GRASS GIS indicated that the maximum likelihood discriminant analysis classifier is the fastest and least accurate one, followed by a sequential maximum a posteriori classifier with reasonably fast execution time and good accuracy. The newly added SVM classifier is the slowest one but provides the highest accuracy and also shows the highest improvement potential by hyperparameter optimization. To illustrate the capabilities of the core classifiers, a showcase is presented where a single Sentinel 2 image is classified to distinguish 18 crop types using all three classifiers available in GRASS.