In this paper, a novel approach for phoneme classification using binary feature vector and correlation based classifier is proposed. The input speech segmentation is carried out using the Average Level Crossing Rate (ALCR) information. A 513-point binary feature vector is generated for each of the phoneme segment detected by ALCR boundaries. The phoneme recognition is based on the uniqueness of the frequency content of each of the phoneme. Instead of using a Hidden Markov Model, Artificial Neural Network or Support Vector Machine based classifier, a simple correlation classifier using the correlation between feature vectors and the set of feature vectors generated with training data is employed. The proposed approach is simpler and requires lesser computational resources when compared with other pattern classification techniques. The performance of proposed phoneme recognition system has been evaluated using real-time speech input and the recognition performance of the proposed phoneme recognition system is satisfactory.