Wireless sensors have been proposed for use in structural health monitoring systems because they offer low-installation costs and automated data processing functionality. A wireless sensor prototype is described for use in large-scale civil structures situated in zones of high seismic activity. When networked together, the distributed computational resources of the wireless sensor network can be leveraged to automate the process of screening post-seismic ambient response data for signs of structural damage. To validate the performance of the proposed wireless monitoring system, a three-story half-scale steel structure is instrumented with a wireless monitoring system assembled from a network of six wireless sensors. Attached to the wireless monitoring system is a heterogeneous array of sensing transducers including strain gages and accelerometers. White noise and seismic ground motion records are applied to the base of the structure using a shaking table. Autoregressive time series models are calculated by the wireless sensors using structural response data. Pattern classification methods are then adopted to classify the structure as damaged or undamaged using the autoregressive time series coefficients as feature vectors. To simulate damage in the structure, the steel columns are modified at the base of the structure with reduced column sections. The proposed damage detection methodology is shown to be capable of identifying the reduced column sections as damage.