The statistical discrimination theory was first proposed by C. R. Rao (1948) and has been widely applied to the several domains of scientific classification. Its theoretical development has been due to the work of C. R. Rao, S. S. Wilks, J. G. Bryan and others. It was recently applied to meteorological classifactory preciction by R. G. Miller (1962-1964) and by the present author (1963-1965) almost concurrently.In this paper, a simple optimum linear discrimination function is firstly investigated for the case containing several qualitative or categorical variables only, based on the joint probability dis-tribution (i. e., multi-variate binomial distribution) defined by A. S. Krishnamoorthy (1951) and by the author (1966).Secondly the quantification procedure maximizing the correlation ratio between classified predictand variable Y and categorical set of predictor variable X used for classification (of Y), which has already been shown by C. Hayashi and S. Chino, is taken into account through com-bining it with a linear discriminant function.Lastly, several explanatory examples are shown for qualitative prediction of the rain state and the occurrence or non-occurrence of snowslide by using the effective presaged predictors.