Classification is one of the important tasks of data mining and neural network is one of the best known tools for doing this task. Despite of producing high classification accuracy, the black box nature of neural network makes it useless for many applications which require transparency in its decision-making process. This drawback is overcome by extracting rules from neural network. Rule extraction makes neural network an alternative to other machine learning methods for handling classification problems by deriving an explanation of how each decision is made. Till now, many algorithms on rule extraction have been proposed but still research on this area is going on to find out more accurate and understandable rules. The proposed algorithm extracts rules from trained neural network for datasets with mixed mode attributes using pedagogical approach. The proposed algorithm uses classified and misclassified patterns to find out the data ranges of significant attributes in respective classes. The experimental results clearly show that the proposed algorithm produces accurate and understandable rules compared to existing algorithms.