This volume provides a comprehensive introduction to the use of neural networks in mechanical engineering applications. Beginning with an overview of different neural network topologies in the first two parts, functioning of human brain is also explained as an analogy with artificial models. Unsupervised models like Hopfield, Bi-directional Associative Memory, fuzzy Associative Memory, Adaptive Resonance Theory, kohonen as well as supervised architectures like Multi-Layer Perceptron, Counter Propagation networks and Radial Basis Function Networks are presented. The third part deals with applications of artificial neural networks for solving of design optimization problems, forward and inverse dynamic analysis applications and system identification and monitoring, as well as motion and vibration control in robotics and structural engineering. Software implementations for neural networks in C/C++ language and necessary optimization techniques in network training are given in Appendices.