The practical channel length for a channel estimator is usually unknown. The maximum possible channel length is therefore assumed a priori. In this paper, we explore the effect of channel information redundancy on the channel estimation accuracy in orthogonal frequency division multiplexing (OFDM) systems. The optimum training sequence for channel estimation is discussed. Theoretical analysis and Monte Carlo simulation are compared and show great agreement. Maximum-likelihood (ML) estimation is used for the channel estimation. The study leads to a novel channel estimator design.