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Ant colony optimization (ACO), inspired by the ants' foraging behavior, is one of the most recent techniques for solving optimization problems. We present an ACO based algorithm for symbol detection in multi-input multi-output (MIMO) system. Since symbol detection is an NP-hard problem so ACO is particularly attractive as ACO algorithms are one of the most successful strands of swarm intelligence and are suitable for applications where low complexity and fast convergence is of absolute importance. Maximum Likelihood (ML) detector gives optimal results but it uses exhaustive search technique. We show that ACO based detector can give near-optimal bit error rate (BER) at a much lower complexity level. The simulation results suggest that the proposed detector gives an acceptable performance complexity trade-off in comparison with ML and VBLAST detectors.