Artificial Neural Networks (ANN) have been used in stock prediction extensively as it provides better results than other techniques. In this paper, different architectures of ANN, namely, simple feed forward back propagation neural network (FFBPNN), Elman Recurrent Network, Radial Basis Function network (RBFN) are implemented and tested to predict the stock price. Levenberg-Marquardt Back-propagation algorithm is used to train the data for both FFNN and Elman Recurrent Network. These techniques were tested with published stock market data of Bombay Stock Exchange of India Ltd., and from the results it is observed that FFBPNN gives better results than Elman Recurrent Network.