We consider the theoretical and practical aspects of locating acoustic sources using an array of microphones. A maximum-likelihood (ML) direct localization is obtained when the sound source is near the array, while in the far-field case, we demonstrate the localization via the cross bearing from several widely separated arrays. In the case of multiple sources, an alternating projection procedure is applied to determine the ML estimate of the DOAs from the observed data. The ML estimator is shown to be effective in locating sound sources of various types, for example, vehicle, music, and even white noise. From the theoretical Cramér-Rao bound analysis, we find that better source location estimates can be obtained for high-frequency signals than low-frequency signals. In addition, large range estimation error results when the source signal is unknown, but such unknown parameter does not have much impact on angle estimation. Much experimentally measured acoustic data was used to verify the proposed algorithms.