Information and energy are at the core of everything around us. Our entire existence is a process of gathering, analyzing, understanding and acting on the information. For many applications dealing with large amounts of data, pattern classification is a key element in arriving at the solution. Engineering applications especially like medical diagnosis require the ability to accurately classify the recorded data for controlling, tracking and decision making. Although modern technologies enable storage of large streams of data, we do not have technology to help us to understand, analyze, or even visualize the hidden information in the data. Data mining is the emerging field that helps us to understand and analyze the hidden information from Very Large Databases (VLDB). In this paper, two important mining tools (i.e.) Neural networks and Genetic algorithm have been used for mining the database through pattern classification. The processing methodology consists of three major phases: Network construction and training, Pruning, Rule extraction and validation. The networks used are namely Adaptive Resonance Theory 1.5, Adaptive Resonance Theory 3 and Multi Channel ART (MART). These networks belong to the Adaptive Resonance Theory (ART) family, which is a self-organizing neural network capable of clustering arbitrary sequence of input patterns into stable recognition codes. The pruning phase aims at removing redundant links and units without increasing the classification error rate of the network. The pruning methods adopted are Local pruning and Threshold pruning.