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The N-FINDR algorithm is one of the most widely used and successfully applied methods for automatically determining endmembers in hyperspectral image data without using a priori information. The algorithm attempts to automatically find the simplex of maximum volume that can be inscribed within the hyperspectral data set. Due to the intrinsic complexity of remotely sensed scenes, the final volume-based solution provided by N-FINDR may be not the global maximum. In addition, the final results provided by the algorithm are typically dependent of its initialization. In this letter, we explore the aforementioned issues and conduct a quantitative and comparative analysis of different (available and new) strategies for the implementation of N-FINDR. Our experimental evaluation and comparison are conducted using two well-known hyperspectral scenes collected by the National Aeronautics and Space Administration Jet Propulsion Laboratory's Airborne Visible Infrared Imaging Spectrometer.