The book outlines the design and application of certain neuro-computing tools for modeling extensive air showers (EAS). Detection and analysis of High Energy Particle Showers involve a plethora of theoretical and experimental works with a host of constraints resulting in inaccuracies in measurements. Therefore, there exist a necessity to develop a readily available system based on soft-computational approaches which can be used for EAS analysis. This is due to the fact that soft computational tools can be trained as classifiers to adapt and learn the surrounding variations. Several learning based structures have been designed and tested to determine the primary energy of EAS and the location. Simulated conditions involving EAS confined to circular arcs of varying radii have been considered for the work. The data inputs are obtained from an array of detectors placed in a circular arrangement resembling a practical detector grid which have a higher dimension and greater correlation among themselves. The results show that the approaches explored are reliable and accurate.