Arti®cial neural networks are widely accepted as a technology oering an alternative way to tackle complex and ill-de®ned problems. They can learn from examples, are fault tolerant in the sense that they are able to handle noisy and incomplete data, are able to deal with non-linear problems and, once trained, can perform prediction and generalisation at high speed. They have been used in diverse applications in control, robotics, pattern recognition, forecasting, medicine, power systems, manufacturing, optimisation, signal processing and social/psychological sciences. They are particularly useful in system modelling, such as in implementing complex mappings and system identi®cation. This paper presents various applications of neural networks in energy problems in a thematic rather than a chronological or any other order. Arti®cial neural networks have been used by the author in the ®eld of solar energy, for modelling the heat-up response of a solar steam-generating plant, for the estimation of a parabolic trough collector intercept factor, for the estimation of a parabolic trough collector local concentration ratio and for the design of a solar steam generation system. They have also been used for the estimation of heating loads of buildings. In all those models, a multiple hidden layer architecture has been used. Errors reported in these models are well within acceptable limits, which clearly suggest that arti®cial neural networks can be used for modelling in other ®elds of energy production and use. The work of other researchers in the ®eld of energy is also reported. This includes the use of arti®cial neural networks in heating, ventilating and air-conditioning systems, solar radiation, modelling and control of power generation systems, load forecasting and prediction, and refrigeration.