These are stochastic learning processes having recurrent structure and are the basis of the early optimization techniques USED in ANN. Boltzmann Machine was invented by Geoffrey Hinton and Terry Sejnowski in 1985. More CLARITY can be observed in the words of Hinton on Boltzmann Machine.“A surprising feature of this network is that it uses only locally available information. The change of weight depends only on the behavior of the two units it connects, even though the change optimizes a global measure” - Ackley, Hinton 1985.Some important points about Boltzmann Machine −They use recurrent structure.They consist of stochastic neurons, which have one of the two possible states, either 1 or 0.Some of the neurons in this are adaptive freestate and some are clamped frozenstate.If we apply simulated annealing on discrete Hopfield network, then it would become Boltzmann Machine.Objective of Boltzmann MachineThe main purpose of Boltzmann Machine is to optimize the solution of a problem. It is the work of Boltzmann Machine to optimize the weights and quantity related to that PARTICULAR problem.ArchitectureThe FOLLOWING diagram shows the architecture of Boltzmann machine. It is clear from the diagram, that it is a two-dimensional array of units. Here, weights on interconnections between units are –p where p > 0. The weights of self-connections are given by b where b > 0.