Breath is the most important and better sample for diagnosing infections and other diseases, the analysis of the breath demonstrate that advantage more than other fluids such as blood. Besides one of the most important technologies used on the tasks of applications of Artificial Neural Networks ANN, is the recognition that uses in many areas such as health, video games, and more. This seminal paper highlighted distinction between two classification method that used to select and analyze features from breath print, the first one is the Mutual Information (MI) which uses an Information-theoretic approach for feature selection, the Mutual Information approach uses the features as input for classifier: support vector machine (SVM),k-nearest neighbors(K-NN),Bayesian Network (BN), Maximum Likelihood (ML) which all discussed in (Wang. et al.,2014), the second one is the time-frequency analyses methods (Wavelet Transform) and frequency analyses (Fourier) approach that is used for feature selection, in addition it uses these features as input to the classifier: machine learning algorithms Artificial Neural Network (ANN) and k-NN are both used and discussed in (Göğüş. et al.,2015) which used to classify the asthmatic breath sounds. Keywords: Breath-print, E-Nose, Auscultation, Mutual Information, Discrete Wavelet Transform, Wavelet Packet Transform, Artificial Neural Network