One of the important tasks in sensor networks is classifying moving vehicles. Fusion of large amount of sensor measurements can improve network performance and reduce the consumption of sensor network resource. We study using continuous measurements of multiple sensor nodes to improve the classification performance by spatio-temporal fusion and fault detection. Time series decisions of single sensor node are aggregated to make a reliable classification estimation. A fusion center combines local classification decisions and evaluates the correctness of these decisions. A correctness status is sent back to each sensor node. Based on the status, sensor nodes can adjust their temporal fusion result. Simulation results demonstrate the validity of our method.