In this paper, we propose a new maximum-likelihood (ML) target location estimator which uses quantized sensor data and wireless channel statistics in a wireless sensor network. The novelty of our approach comes from the fact that imperfect channel statistics between wireless sensors and the fusion center are incorporated in the localization algorithm. We call this approach "channel-aware target localization". Furthermore, we derive the Cramer-Rao lower bound as a performance bound for our channel-aware ML estimator. Simulation results are presented to show that the performance of the channel-aware ML location estimator is quite close to its theoretical performance bound even with relatively small number of sensors and it has superior performance compared to that of the channel-unaware ML estimator.