In the presented study established single tree detection methods are benchmarked and investigated. In total eight airborne laser scanning (ALS) based detection methods were applied to a unique dataset originating from different regions of the Alpine Space covering different study areas, forest types and structures. The evaluation of the detection results was carried out in a clear and reproducible way by automatically matching the detection results to precise in-situ forest inventory data. Quantitative statistical parameters such as the percentages of correctly matched trees and omission and commission errors are presented. The benchmarking results are prepared in complementary levels of information, starting with the analysis based on study area as well as detection method. Additionally investigations per forest type and an overall performance of the benchmark are presented. The best matching rate was obtained for single layered coniferous forests. Trees in lower height layers were challenging for all tested methods. The overall performance shows a matching rate of 47% which is comparable to results of other benchmarks performed in the past for other forest types. The study brings new hindsight regarding the potential and limits of tree detection with ALS and underlines some key aspects regarding the choice of method when performing single tree detection for the various forest types encountered in alpine regions.