Life would be much easier if there were no typing involved in preparing a document, typing an email, paying online bills, entering credit card details, booking flights, hotels or car rentals online. Imagine a system that would recognize speech and convert it into another form to do these functions automatically. The fact that most people can speak faster than they can type gives a good reason to have a speech recognizer. This thesis concentrates on developing a speaker independent, speech recognizer using Wavelet Packet Transform. Speech corpus in the form of phonemes is collected from an American male and an Indian Female. The subjects chosen for phoneme recognition vary in a number of factors like the accent, gender, age, microphone used to record speech, environment in which phonemes are recorded, etc. These factors increase the complexity of speech recognition. We also assume that the emotions of the speakers are the same and the speakers are stationary while recording phonemes.