In this paper, an efficient and reliable feature-fusion-based modulation classification (MC) algorithm for multiple-input multiple-output (MIMO) systems is developed. It uses two higher-order cumulants of the transmitted signal streams to classify a broad set of modulation types with no prior knowledge of the channel state information. We address the problem of the soft-decision fusion for the feature-fusion-based MC algorithms for MIMO systems and introduce an optimal soft-decision fusion scheme to find the classification result. The complexity order of the proposed MC algorithm is studied in detail to demonstrate its low computation cost, and its performance is validated extensively by simulation results to show its practical effectiveness. © 2017, King Fahd University of Petroleum & Minerals.