Recent contributions in the area of Automatic Speech Recognition (ASR) for Indian Languages has been increased. This paper serves as a comprehensive study of different feature extraction methods namely MFCC, PLP, RASTA-PLP and PNCC. An attempt to find out which of these front end techniques performs better for real world Indian Language data is analyzed experimentally. Then, an isolated word recognizer is built for three Indian languages (i.e., Tamil, Assamese and Bengali) under real world conditions and investigates the importance of handling long silence using segmentation method. The experimental analysis shows that PNCC provides better performance for clean data whereas MFCC shows improved performance in case of multi-condition speech data. © 2014 IEEE.