The dynamic properties of bridges can be affected not only through damage but also from ambient uncertainty. False-positive or negative alarms may be raised if environmental effects are not considered in the detection algorithm. This article presents a two-step data-driven approach that can incorporate temperature effects in vibration-based damage detection and localization, provided the temperature is also measured. To detect the occurrence of damage, prediction errors of an autoassociative neural network (AANN) framework are first employed as a temperature-invariant novelty index (NI). Further, for damage localization, NIs associated with each of the damage cases, are classified using a radial basis function neural network (RBFNN). The proposed algorithm is numerically tested on a multispan bridge structure involving measurement uncertainties. The performance of the RBFNN classifier is further assessed using different classifier performance metrics. The possibility of positive and negative false alarms raised by the proposed algorithm and its sensitivity to measurement noise contamination is also investigated. It is observed that the proposed data-based approach can efficiently isolate a fault, even in the presence of temperature variation.