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An artificial neural network (ANN) is a wide class of flexible and simple mathematical model. It is capable to identify complex nonlinear relationships between input and output observed datasets. Neural network consists of a large number of “neurons,” nonlinear computational elements, connected internally in a complex way and arranged into layers [7]. Artificial neural network simulates natural neural network in the brain. In the rain, the fundamental neural network is connected to each other by synapses. The neurons are basic components of the human brain are processing unit in the brain. The neurons are responsible for learning and retention of information. The sensory/observed data are the input to the network, processes it, and gave output for other neurons. In ANN, everything is designed to replicate this process. An ANN also consists of a bundle of neurons. Biological axon-dendrite connects each node to other nodes via links. All the data the variable name “X” enters in the system with a weight of “w” for generating a weighted value. Each link weight determines the strength of nodes influence on other node. This denotes the strength of a signal in the brain. An activation function that use the basic mathematical equations to determine input-output relation. The familiar activation functions in NN are logistic function, binary step function, rectified linear units, and hyperbolic tangent function. The ANN models are efficient; particularly in solving the problem in the complex processes, which are difficult to describe using physical equations [8]. The ANN models are capable of modeling the complex nonlinear relationships, compared with a traditional linear regression model approach [9]. The ANN also has excellent fault tolerance and is fast and highly scalable with parallel processing. ANN models are similar to statistical models like generalized linear regression models, polynomial models, nonparametric models and discriminant analysis, principal component analysis, and other models in which the prediction of complicated phenomena is important than the explanation. On the other hand, NN models, like learning vector quantization, counter propagation, and self-organizing maps, are useful for data analysis. Some of the published work that provide insight about relation between statistics and NN are discussed.