During my Ph.D study I had been challenged by many pattern recognition tasks which coming from very different disciplines. Each task had it's unique problems. It took great efforts to find a suitable way to solve each of the problem. In return, this thesis has covered a very broad spectrum of Chemometrics pattern recognition techniques, from well known and commonly used models such as PCA, PLS-DA to state of the art models such as kernel learning and support vector machines. There is also a good coverage of the methods which are much less known to the community but could be very useful methods such as principal coordiante analysis, featureless pattern recognitions and multivariate pattern comparison. The usefulness of these methods were demonstrated through several real-life applications including single nucleotide polymorphism analysis, DNA microarray analysis and a few human and mice metabolomic profiling studies. The comprehensive coverage of chemometrics pattern recognition techniues combined with several applications could be very useful to those who work in a similar discipline.