Structural modal property gets affected not only due to damage but also variation inambient temperature, humidity etc. Detection of damage through modal comparison thus may lead to false predictions. This article presents a two-stage data-driven approach in which damage detection and localization are performed in consequence. For detection, an auto-associative neural network (AANN) has been developed and its prediction error is defined as the novelty index. Each unique damage pattern has been observed to demonstrate a unique pattern in the prediction error and this pattern is also found to be consistent under different ambient temperatures. This prediction error is, therefore, modelled using a second artificial neural network with radial basis function as its activation function in order to classify the damage against associated patterns in AANN prediction error which in turn helped in localizing the damage. Proposed algorithm is further validated using numerical experiments.