Summary: This paper presents a comprehensive simulation study which aims to profile the performance capabilities of the global-local hybrid ensemble in comparison with leading ensemble classifiers as reported in recent studies in the literature. The global-local hybrid ensemble is implemented with decision tree (global) and nearest-neighbor (local) base learners and its accuracy performance is compared, on 46 benchmark datasets from the UCI machine learning repository, to those of other ensembles from six prominent studies in the literature. Through statistical significance testing, it is shown that the global-local hybrid ensemble is a robust classifier design: over a larger spectrum of data domains, it performs competitively with other leading ensembles.