The purpose of this study is to establish an agricultural regionalization of the southern part of Fukuoka Prefecture by using both empirical and objective methods. The studied area is divided into the vicinity of the Chikugo river and the adjoining mountainous region. This area is characterized by a warm and heavy rainy climate and high soil fertility. It has been known as a leading cereal producing district in Japan. The area is comprised of 35 administration units (cities, towns and villages). Firstly, to make the economic regionalization of this area, 18 variables of the socio-economic and environmental factors were chosen for the principal component analysis (PCA). The analysis shows that 80.73 % of the total variance could be explained by the first, second and third principal components. Some considerations were paid to the interpretation and the scores of these components. The first principal component could explain 50.71 % of the total variance. The variables having a positive correlation with this component were the indices of population density and the industrial worker indices. The variables having negative correlation with this component were mainly the rural-related ones. Therefore, this principal component offered the aggregate index of urbanization. The second principal component which could explain the total variance of the original data by 19.35 % might be interpreted as the aggregate index showing the degree of dependency of the agricultural sector. The third principal component, explaining 10.67 % of the total variance, showed the weakness of the character of both villages in the plain region and those in the mountainous region. The scores of these three principal components were used for the cluster analysis (CA) by means of grouping the 35 administration units and, as a result, 5 regions (the suburb, the plain, semi-plain, semi-mountainous, and mountainous) were obtained (Fig. 3). Secondly, to make the regionalization considered the comprehensive agricultural development of this area, 22 variables related to agriculture and farms were chosen for the PCA. Similarly, the interpretation and the scores of the components were considered. The first principal component could explain 46.97 % of the total variance. This principal component showed the contrast between the large-scale farms in the farm labor abundant region and the other farms. It, therefore, gave the aggregate index for the scale of farm business. The second principal component had the share of 17.80 % of the total variance of the origianl data. It gave the aggregate index of productivity. The scores of these two principal components were used for the CA by means of grouping the 35 administration units and, as a result, 5 regions (I, II, III, IV and V) were obtained (Fig. 5). Finally, the combination between the economic regionalization and the regionalization based on the comprehensive agricultural development was considered in order to seek for the final regionalization. The result suggested the regionalization of the studied area into 11 district regions (Table 9).