Exposure to volatile organic chemicals (VOCs) in drinking water has been linked to a number of adverse health effects including cancer, liver, and kidney damage. However, the large number of potential contaminants and the cost and complexity of existing analytical methods limits the extent to which water quality is routinely characterized. This project focused on the laboratory development and evaluation of an instrument for field analysis of VOCs in drinking water. The instrument is based on an array of six polymer-coated surface-acoustic-wave microsensors. A test-set consisting of dichloromethane, chloroform, 1,1,1-trichloroethane, perchloroethylene, and m-xylene was used in a series of experiments designed to optimize the purge–trap preconcentration system, calibrate the instrument over the concentration range of 0.2–2 times the USEPA maximum contaminant levels (MCLs), and compare results to those of a reference laboratory. The primary goal was to develop a cost-effective alternative for on-site evaluation of VOCs in water. Calibration and evaluation test results for spiked water samples demonstrate adequate sensitivity for 19 of the 21 regulated VOCs considered using a ten minute sampling and analysis cycle. Monte Carlo simulations characterized the performance of trained artificial neural networks (ANNs) which had correct classification rates of 99%, 90%, and 80% for the five individual test-set vapors and their binary and ternary mixtures, respectively. These results demonstrate the excellent potential of this technology for addressing the need for improved VOC field-screening methods for water supplies.