Artificial neural networks are computer systems based on mathematical models which are inspired by biological neural networks, and find their application in the fields of artificial intelligence, data processing, robotics and many other fields of science. They consist of one input and one output layer, and one or more hidden layers. The number of input layer neurons is determined by the size of the input data, while the number of output neurons is equal to the number of classes we want to decide. The number of hidden layers and the number of neurons in each hidden layer is problem specific, and is one of the most important parameters that has to be carefully chosen. Weights between layers are initialized randomly using some random distribution function. Activation function is used to map neural network output to some desired range. Learning rate is one of the most important parameters related to neural networks. Learning rate determines the speed of learning process, as well as final prediction ability. Neural networks work on the principle of supervised learning, which refers to the fact that in the learning process we give the network labeled data . Neural network give very good results in the process of object recognition and classification.