The paper proposes a QCD-motivated 'k⊥' jet-clustering algorithm for hadron-hadron collisions that is invariant under boosts along the beam directions. This invariance improves factorization properties and aligns better with experimental practices at hadron colliders. The authors examine alternative definitions of resolution variables and recombination schemes, showing that the algorithm can be efficiently implemented on a computer to provide a full clustering history of each event. Using simulated data at \(\sqrt{s} = 1.8\) TeV, they study the effects of calorimeter segmentation, hadronization, and the soft underlying event, comparing the results with those obtained using a conventional cone-type algorithm. The paper also discusses the theoretical and phenomenological aspects of the algorithm, including its implementation and numerical studies.The paper proposes a QCD-motivated 'k⊥' jet-clustering algorithm for hadron-hadron collisions that is invariant under boosts along the beam directions. This invariance improves factorization properties and aligns better with experimental practices at hadron colliders. The authors examine alternative definitions of resolution variables and recombination schemes, showing that the algorithm can be efficiently implemented on a computer to provide a full clustering history of each event. Using simulated data at \(\sqrt{s} = 1.8\) TeV, they study the effects of calorimeter segmentation, hadronization, and the soft underlying event, comparing the results with those obtained using a conventional cone-type algorithm. The paper also discusses the theoretical and phenomenological aspects of the algorithm, including its implementation and numerical studies.