In Part I of this paper we briefly review the history of seasonal adjustment and statistical time series analysis in order to understand why seasonal adjustment methods have evolved into their present form. This provides insight into some of the problems that must be addressed by seasonal adjustment procedures, and points out that advances in modern time series analysis raise the question of why seasonal adjustment should be performed at all. This leads to a discussion in Part II of issues involved in seasonal adjustment. We state our own opinions about the issues raised and review some of the work of other authors. First, we comment on reasons that have been given for seasonal adjustment and suggest a new possible justification. Then we emphasize the need to precisely define the seasonal and nonseasonal components and offer our own definitions. Finally we discuss criteria for evaluating seasonal adjustments. We contend proposed criteria based upon empirical comparisons of estimated components are of little value, and suggest that seasonal adjustment methods can be evaluated based upon whether or not they are consistent with the information in the observed data. This idea is illustrated with an example.