Artificial neural network, in short, â€˜Neural Networkâ€™, is a network of simple processing units called artificial neurons, which simulate the functioning of human brain â€“ an awe-inspiring creation on Earth. Neural network is used in artificial intelligence and has been traditionally been viewed as a simplified model of neural processing in the brain. Neural networks have a remarkable ability to derive meaning from complicated and imprecise data. They can be utilized to extract patterns and detect trends that are too complex to be deciphered either by human beings or computer devices. Though network simulations have been taken cognizance of in the recent past, the phenomenon was well established even before the advent of computers. In the terminology of currently available software packages, these artificial neurons are called "processing elements" and have many more capabilities than the simple artificial neurons. The potential and usefulness of artificial neural networks have been demonstrated in several applications, including speech synthesis, diagnostic problems, medicine, business and finance, robotic control, signal processing, computer vision and many other areas that fall under the category of pattern recognition. In some application areas, neural models show promise in achieving human-like performance and reveal an edge over traditional artificial intelligence techniques. The book will serve as a curtain raiser for the concept of neural network covering a brief history, stages of development, and its architecture. It also provides some cases and applications of the concept including its importance, the range of coverage in terms of applications and its utility in areas where complex thinking and analysis is required. It is hoped that the book will trigger the study of the subject in all its ramifications.