In this dissertation, the problem of symbol timing synchronization for the following three different communication systems is studied: 1) conventional single-carrier transmissions with single antenna in both transmitter and receiver; 2) single-carrier transmissions with multiple antennas at both transmitter and receiver; and 3) orthogonal frequency division multiplexing (OFDM) based IEEE 802.11a wireless local area networks (WLANs). For conventional single-carrier, single-antenna systems, a general feedforward symbol-timing estimation framework is developed based on the conditional maximum likelihood principle. The proposed algorithm is applied to linear modulations and two commonly used continuous phase modulations: MSK and GMSK. The performance of the proposed estimator is analyzed analytically and via simulations. Moreover, using the newly developed general estimation framework, all the previously proposed digital blind feedforward symbol timing estimators employing second-order statistics are cast into a unified framework. The finite sample mean-square error expression for this class of estimators is established and the best estimators are determined. Simulation results are presented to corroborate the analytical results. Moving on to single-carrier, multiple-antenna systems, we present two algorithms. The first algorithm is based on a heuristic argument and it improves the optimum sample selection algorithm by Naguib et al. so that accurate timing estimates can be obtained even if the oversampling ratio is small. The performance of the proposed algorithm is analyzed both analytically and via simulations. The second algorithm is based on the maximum likelihood principle. The data aided (DA) and non-data aided (NDA) ML symbol timing estimators and their cor- responding CCRB and MCRB in MIMO correlated ??at-fading channels are derived. It is shown that the improved algorithm developed based on the heuristic argument is just a special case of the DA ML estimator. Simulation results under different operating conditions are given to assess and compare the performances of the DA and NDA ML estimators with respect to their corresponding CCRBs and MCRBs. In the last part of this dissertation, the ML timing synchronizer for IEEE 802.11a WLANs on frequency-selective fading channels is developed. The proposed algorithm is compared with four of the most representative timing synchronization algorithms, one specically designed for IEEE 802.11a WLANs and three other algorithms designed for general OFDM frame synchronization.