A common problem in Data Mining (DM) is the presence of noise in the data being mined. Artificial neural networks (ANN) are robust and have a good tolerance to noise, which makes them suitable for mining very noisy data. Although they may achieve high classification accuracy, they have the well-known disadvantage of having black-box nature and not discovering any high-level rule that can be used as a support for human understanding. The main challenge in using ANN in DM applications is to get explicit knowledge from these models. For this purpose, a study on knowledge acquirement from trained ANNs for classification problems is presented. The proposed method uses Touring Ant Colony Optimization (TACO) algorithm for extracting accurate and comprehensible rules from databases via trained artificial neural networks. The suggested algorithm is experimentally evaluated on different benchmark data sets. Results show that the proposed approach has a potential to generate accurate and concise rules.