Prematurity, especially if extreme, is one of the leading causes of problems and/or death after delivery. Among all the problems encountered by premature infants, feeding difficulties are very common. Many premature infants are fed intravenously at first, and they progress to milk feedings provided by a tube passed into the stomach. At around 34 weeks of gestation, premature infants should be able to breastfeed or take a bottle. At the same time such premature infants are usually faced with difficulty making the transition from tube-feeding to full oral feeding. In this study three physiological measurements of premature infants including sucking, swallowing and breathing were measured. The objective of this work was to detect, identify and classify these three signals independently and in relation to each other. The goal was to look at the specification of sucking, swallowing and breathing signals to extract the ratio of suck swallow-breath coordination. The results of this study were used to predict the readiness of a premature infant for introduction to oral feeding.To accomplish this, three different methods were examined. In the first method, the integration of the wavelet packet transform and a neural network was investigated. According to results of the first approach, integration of the wavelet packet transform and the neural network failed due to the inefficiency of the feature extraction method. Thus, the wavelet packet energy nodes did not provide a good feature extraction tool in this specific application.In the second approach, the frequency content of each signal was investigated to study the relationship between the shape of each waveform and the frequency content of that specific signal. Spectral analysis for suck, swallow and breathing signals showed that the shape of the signal was not tightly related to the frequency content of that specific waveform. Therefore, the frequency content could not be used as a method of feature extraction in this specific application.In the third method, the integration of correlation and matched filtering techniques was investigated and demonstrated promising result for the detection of suck and breathing signal but not for the swallowing waveform. Based on the results for sucking and breathing signals, this method should also work for good quality swallowing signal. To understand the relationship between the suck, swallow and breathing signals a matrix containing information on the time of occurrence of each event was developed.