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A new distributed fusion filtering algorithm for linear multiple time-delayed systems is proposed. The multisensory distributed fusion filter is formed by the summation of local Kalman filters having time delays (LKFTDs) in both the system and measurement models. The proposed distributed filter has a parallel structure that enables processing of multisensory measurements; thereby, it is more reliable than the centralized version if some sensors turn faulty. The key contribution of this paper is the derivation of recursive error cross-covariance equations between the LKFTDs to compute the optimal matrix fusion weights. In the particular case of multisensory dynamic systems having time delays in only the measurement model, the obtained results coincide with the previous work of Sun. The high accuracy and efficiency of the proposed distributed filter are then demonstrated through its implementation on a vehicle suspension system.