REVEALS-based quantitative reconstruction of Holocene vegetation cover (expressed in plant functional types, PFTs) is used in the LANDCLIM project to assess the effect of human-induced land-cover change on past climate in NW Europe. Using the Czech Quaternary Pollen Database, this case study evaluates the extent to which selection of data and input parameters for the REVEALS model applications would affect reconstruction outcomes. The REVEALS estimates of PFTs (grid-cell based REVEALS PFT estimates, GB REVEALS PFT-s) are calculated for five time windows of the Holocene using fossil pollen records available in each 1° × 1° grid cell of the Czech Republic. The input data and parameters selected for testing are: basin type and size, number of 14C dates used to establish the chronology of the pollen records, number of taxa, and pollen productivity estimates (PPE). We used the Spearman correlation coefficient to test the hypothesis that there is no association between GB REVEALS PFT-s using different data and parameter inputs. The results show that differences in the basin size and type, number of dates, number and type of taxa (entomophilous included or not), and PPE dataset do not affect the rank orders of the GB REVEALS PFT-s significantly, except for the cases when entomophilous taxa are included. It implies that, given careful selection of data and parameter and interpretation of results, REVEALS applications can use pollen records from lakes and bogs of different sizes together for reconstruction of past land cover at the regional to sub-continental spatial scales for purposes such as the study of past land cover–climate interactions. Our study also provides useful criteria to set up protocols for data compilation REVEALS applications of this kind.