It has been demonstrated that applying intervention analysis can aid in time series decomposition, in particular seasonal adjustment (e.g., Buszuwski and Scott, 1988 and Findley et al, 1988). However, there can be pitfalls in modeling interventions, such as (1) unusual behavior occurring near the end of the series, when the nature of the intervention is unclear and (2) difficulties in selecting a set of interventions for a series. for instance, illustrating (1), if seasonality is changing, but one or more additive outliers are modeled, intervention analysis will tend incorrectly to reinforce the previous seasonal pattern. Regarding (2), there may be conflicts between simplicity or parsimony and diagnostic statistics form modeling and seasonal adjustment. These issues are examined in the framework of X-11-ARIMA seasonal adjustment with price index series form the Bureau of Labor Statistics.