In the age of digital era, machine learning is much more influence and popular rather than case based methods. Among the machine learning research area neural network is very popular for its classification accuracy and learning rate on the complex environment. The important remark on the neural network classification is to use the big volume of data size for training and its data types. The fundamental working concept of neural networks is learning and training of the data computing system. Neural Network is composed of a bulk number of interconnected computing elements. This paper mentions that the properties of the two type of neural networks: Radial Basis Function (RBF) and Back Propagation (BP) neural networks are analyzed and compared based on mean square error, accuracy and nature of datasets concerning with attribute types. There are 15 datasets in this paper are used from UCI machine learning repository.