Datasets containing large numbers (>10 000) of glacial lineaments are increasingly being mapped from remotely sensed data in order to develop a palaeo-glacial reconstruction or ‘inversion’. The palimpsest landscape presents a complex record of past ice flow and deconstructing this information into a logical history is an involved task. One stage in this process requires the identification of sets of genetically linked lineaments that can form the basis of a reconstruction. This paper presents a semi-automated algorithm, CLustre, for lineament clustering that uses a locally adaptive, region growing, methodology. After outlining the algorithm, it is tested on synthetic datasets that simulate parallel and orthogonal cross-cutting lineaments, encompassing 1500 separate classifications. Results show robust classification in most scenarios, although parallel overlap of lineaments can cause false positive classification unless there are differences in lineament length. Case studies for Dubawnt Lake and Victoria Island, Canada, are presented and compared with existing datasets. For Dubawnt Lake 9 out of 14 classifications directly match incorporating 89% of lineaments. For Victoria Island 57 out of 58 classifications directly match incorporating 95% of lineaments. Differences are related to small numbers of unclassified lineaments and parallel cross-cutting lineaments that are of a similar length. CLustre enables the automated, repeatable, assignment of lineaments to flow sets using defined user criteria. This is important as qualitative visual interpretation may introduce bias, potentially weakening the testability of palaeo-glacial reconstructions. In addition, once classified, summary statistics of lineament clusters can be calculated and subsequently used during the reconstruction process.