So far a single-stage neural network has been proposed to forecast the insolation of the next day. The mean error of the forecast insolation by the single-stage neural network is about 30%. In this paper, a multi-stage neural network is developed for further reduction of the mean error. A first-stage neural network forecasts the average atmospheric pressure of the next day from atmospheric pressure data of the previous day. A second-stage neural network forecasts the insolation level of the next day from the average atmospheric pressure and weather data of the previous day. A third-stage neural network forecasts the insolation of the next day from the insolation level and weather data of the previous day. Meteorological data at Omaezaki, Japan in 1988-1993 are used as input data, and the insolations in 1994 are forecast. The insolations forecast by the multi-stage and the single-stage neural networks are compared with the measured ones. The results show that the mean error reduces from about 30% (by the single-stage) to about 20% (by the multi-stage).