A Bayes procedure for classifying an observation consisting of one dichotomous variable (X) and a continuous vector Y is applied to a model assuming that the conditional distribution of Y given X is normal. The procedure reduces to two linear discriminant functions, one for each value of X. An example utilizing data on critically ill patients is given. Extension to one polytomous variable or several dichotomous variables is discussed.