This paper showcases the application of Neural Networks for manipulation. Algorithm based approaches work well although are limited in their ability to find solutions sometimes. The tedious task of programming a manipulator can be replaced with Neural Networks which can learn how to solve Inverse Kinematics. We present a pick and place scenario using Neural Network to solve Inverse Kinematics. Their ability to learn from examples make them a good candidate to solve the inverse kinematics problem. For this purpose, a unique configuration of 5 DOF arm is designed to suit industrial needs. To train the network, a dataset of random joint positions is created and forward kinematics is derived for the corresponding joint angles. The joint variables are then fed to a path planner in Simulink and then the process is simulated.