Abstract: Neural network architecture is widely used in computer science for solving complex problems arise in various research applications. One of the popular applications of neural network is pattern recognition. Neural network can be used to solve real world complex problems that can be solved by human being among which some require less effort while others require tremendous efforts. If neural network is incorporated with phonetic algorithms that recognize whether the given two words are phonetically similar or not yields improved outcome. The integrated approach discussed here results in performance improvement over any single algorithm implementation. Different phonetic algorithms are developed to identify phonetic similarity with different set of rules and different set of output after processing English words. Homophones are the English words having similar pronunciation but different spelling and meaning. The number of such phonetic algorithms are studied and discussed briefly here and then another algorithm with phonetic rules is built. Each algorithm has different level of performance. The performance word used here is in the context of identifying the homophones but not the processing performance of algorithm performed by a computer system. Each algorithm has advantages and limitations but by incorporating these algorithms in neural network results in improvement compared to any single algorithm implementation. Neural networks are commonly used for solving pattern recognition problems and so the efforts are being made here to recognize similar English words pattern found in pronunciation of the English words.