© 2015 MTS. In this paper, we propose an ICT application of real-time monitoring system to estimate the catch amount within set-net. Generally, set-net fishermen do not know the catch amount in advance before arriving to set-net area, in which they tend to unable to perform the fishing operation effectively. It means that the fishermen might suffer a loss of time, gasoline and labor cost, along with extra effort. In order to support the fishermen to avoid such condition, in our previous study, we have presented the real-time monitoring system based on ICT aided echo sounder for the set-net fishing. Besides, the algorithm based on multiple regression analysis (MRA) for estimation of catch amount was proposed so that fishermen could predict the catch amount before they leave the port for harvesting. However, the catch estimation was not sufficiently accurate in practical application. The reason of this is because the daily catch amount varies from some few tons to 250 tons, whereas statistics of ping intensity do not vary so much, thus, it is not easy to build a model of MRA adequately. This study aims to develop a more accurate catch estimation algorithm based on our previous study. The experiment of this study is conducted in Hokkaido and Toyama Prefecture. To transform the catch record appropriately for MRA, Box-Cox transformation (BCT) as pre-processing transformation is applied in this paper. The catch estimation has been done based on fishermen's empirical estimation through a pre-processing transformation and statistical analysis of reflection data in each layer depth. The study showed that the present system could improve the estimation accuracy. In order to measure the differences between catch estimation by the developed algorithm and the real catch record, Relative Absolute Error (RAE) is used in this study. The RAE for Hokkaido experimental site was 1% and in Toyama is 22%, respectively. The results show that the estimation accuracy of the present method is sufficiently adequate for practical application.