Seasonality in economic time series can ‘obscure’ movements of other components in a series that are operationally more important for economic and econometric analyses. In practice, one often prefers to work with seasonally adjusted data to assess the current state of the economy and its future course. This paper presents a seasonal adjustment program called CAMPLET, an acronym of its tuning parameters, which consists of a simple adaptive procedure to extract the seasonal and the non-seasonal component from an observed series. Once this process is carried out there will be no need to revise these components at a later stage when new observations become available. The paper describes the main features of CAMPLET. We evaluate the outcomes of CAMPLET and X-13ARIMA-SEATS in a controlled simulation framework using a variety of data generating processes and illustrate CAMPLET and X-13ARIMA-SEATS with three time series: U.S. non-farm payroll employment, operational income of Ahold and real GDP in the Netherlands.