We propose a transfer-learning multi-input multi-output (TL-MIMO) scheme to significantly reduce the required training complexity for converging the equalizers in mode-division multiplexing (MDM) systems. Based on a built three-mode (LP₀₁, LP_11a, and LP_11b) multiplexed experimental system, we thoughtfully investigate the TL-MIMO performances on the three-typed data, collecting from different sampling times, launching optical powers, and inputting optical signal-to-noise ratios (OSNRs). A dramatic reduction of approximately 40%–83.33% in the required training complexity is achieved in all three scenarios. Furthermore, the good stability of TL-MIMO in both the launched powers and OSNR test bands has also been proved.