Acoustic imaging is difficult to achieve in noisy and reverberant environments. Microphone arrays offer an effective approach to obtaining a clean recording of desired acoustic signals in these environments. In this thesis, we have designed, implemented, and evaluated a 64-node microphone array system for acoustic imaging. We have applied a delay-and-sum beamforming algorithm for sound source amplification in a noisy environment, and have explored the uses of the array and beamformer by generating the sound intensity map to reconstruct the acoustic scene. Our experimental results show a mean error of 1.1 degrees for sound source localization, and a mean error of 13.1 degrees for source separation. In addition, we also used the system to image seven different materials with audible sound, and obtained their reconstructed acoustic maps as well as frequency response curves, from which we are able to detect the differences between textures based on their acoustic response powers.