The problem we address in this paper is, whether the feature extraction module trained on large amounts of task independent data, can improve the performance of stochastic models? We show that when there is only a small amount of task specific training data available, tandem features trained on task independent data give considerable improvement over Perceptual Linear Prediction (PLP) cepstral features in Hidden Markov Model (HMM) based speech recognition systems.