Knowledge discovery from sensor data is an important research area due to huge demand of application. In this work the architecture of sensor data mining is represented. Supervised machine learning is an important task in data mining. Therefore the selected supervised learning algorithms such as Multi-Layer Perception (MLP), Radial Basis Function (RBF) network and k-Nearest Neighbor (k-NN) are augmented for the sensor data. The accuracies are presented in term of statistical parameters as correlation coefficient, root means square error etc.