We describe and implement a data selection algorithm aimed at identifying background atmospheric CO2 observations from in situ continuous measurements. Several selection criteria for detecting the background data have been developed and are currently used: the main objective of this work was to define a common methodology to extract the atmospheric background signal minimizing heterogeneities due to the use of different selection algorithms. The algorithm used in this study, (BaDS, Background Data Selection) was tested and optimized using data (from 2014 to 2018) from four Italian stations characterized by markedly different environmental conditions (i.e., mountain, coastal and marine): Plateau Rosa (PRS), Mt. Cimone (CMN), Capo Granitola (CGR) and Lampedusa (LMP). Their locations extend from the Alps to the central Mediterranean. The adopted algorithm proved to be effective in separating the local/regional from the background signal in the CO2 time series. About 6% of the data at LMP, 11% at PRS, 20–38% at CMN and 65% at CGR were identified as non-background. LMP and PRS can be used as reference sites for the central Mediterranean, while CMN and CGR were more impacted by regional sources and sinks. Finally, we discuss a possible application of BaDS screened data.