Recent challenges and errors in fingerprint identification have highlighted the need for assessing the information content of a papillary pattern in a systematic way. In particular, estimation of the statistical uncertainty associated with this type of evidence is more and more called upon. The approach used in the present study is based on the assessment of likelihood ratios (LRs). This evaluative tool weighs the likelihood of evidence given two mutually exclusive hypotheses. The computation of likelihood ratios on a database of marks of known sources (matching the unknown and non-matching the unknown mark) allows an estimation of the evidential contribution of fingerprint evidence. LRs are computed taking advantage of the scores obtained from an automated fingerprint identification system and hence are based exclusively on level II features (minutiae). The AFIS system attributes a score to any comparison (fingerprint to fingerprint, mark to mark and mark to fingerprint), used here as a proximity measure between the respective arrangements of minutiae. The numerator of the LR addresses the within finger variability and is obtained by comparing the same configurations of minutiae coming from the same source. Only comparisons where the same minutiae are visible both on the mark and on the print are therefore taken into account. The denominator of the LR is obtained by cross-comparison with a database of prints originating from non-matching sources. The estimation of the numerator of the LR is much more complex in terms of specific data requirements than the estimation of the denominator of the LR (that requires only a large database of prints from an non-associated population). Hence this paper addresses specific issues associated with the numerator or within finger variability. This study aims at answering the following questions: (1) how a database for modelling within finger variability should be acquired; (2) whether or not the visualisation technique or the choice of different minutiae arrangements may influence that modelling and (3) what is the magnitude of LRs that can be expected from such a model. Results show that within finger variability is affected by the visualisation technique used on the mark, the number of minutiae and the minutiae configuration. They also show that the rates of misleading evidence in the likelihood ratios obtained for one of the configurations examined are low.