"Pattern recognition is the research area that studies the operation and design of systems that machine diagnostics, person identification and industrial inspection." "A branch of artificial intelligence concerned with the classification or description of observations. Pattern recognition aims to classify data (patterns) based on either a priori knowledge recognize patterns in data. It encloses sub disciplines like discriminant analysis, feature extraction, error estimation, cluster analysis (together sometimes called statistical pattern recognition), grammatical inference and parsing (sometimes called syntactical pattern recognition). Important application areas are image analysis, character recognition, speech analysis, man and or on statistical information extracted from the patterns. The patterns to be classified are usually groups of measurements or observations, defining points in an appropriate multidimensional space." Pattern recognition is the science of making inferences based on data. Two of the main forms of pattern recognition are classification and regression. In classification problems, data are collected and given discrete class labels. In a regression problem, on the other hand, data labels are typically continuous values, not categorical. Basic pattern recognition approaches seek a function, f, that takes an observation and predicts the unseen label, y:=f(xj). The goals of learning in pattern recognition are to develop the function, f, given only a (possibly small) set of training data, X={{xi,yi}}, I=1....N. As such, pattern recognition is fundamentally an ill-posed problem, since it is trivially easy to define a function that performs arbitrarily well on the training data (( ).The present paper reviews the techniques for automated extraction of information from signals. The techniques may be classified broadly into two categories—the conventional pattern recognition approach and the artificial intelligence (AI) based approach.