There are around 180 different currencies used in different countries around the world. Currency Recognition and conversion system is implemented to reduce human power to automatically recognize the amount monetary value of currency. Automatic currency note recognition invariably depends on the currency note characteristics of a particular country and the extraction of features directly affects the recognition ability. In this project, the pixel values of 4 different corners of the currency is used as a feature for robust representation of the currency image. Altogether, 2500 features are obtained and is fed to the Neural Network for training purpose. Also, Red, Green and Blue (RGB) values and the size of the currency are additional features hence making the total feature count of 2505. However, adding those features gives less accuracy as compared to the 2500 feature set. A class MLPClassifier is used which implements a Multi-Layer Perceptron (MLP) algorithm that trains using three-layer feed forward Backpropagation. Classification is accepted in seven denomination classes which are Rs.5, Rs.10, Rs.20, Rs.50, Rs.100, Rs.500, and Rs.1000 rupee notes. The currency recognition system obtains an accuracy of 94% on 2500 input nodes and 88% accuracy on 2505 input nodes which includes RGB values and the size.