In this study, we propose a new algorithm which works in Lorentzian space with a similar sense in the k-NN method. We exploit the distance metric of Lorentzian space in classification problem. It is a special metric which may give a zero distance for far points. To take best benefit from structural and other properties of the Lorentzian space, a special projection over the data sets is applied. By this projection, basic geometrical operations are used; namely translation (shifting), compression and rotation. Our new algorithm does classification according to the nearest neighbor in Lorentzian space. The usability and validity of the proposed classification method is tested by some public data sets such as WHOLE, VERTEBRAL, RELAX, ECOLI. The results are compared with results of well-known classical classification methods such as kNN, LDA, SVM and Bayes. As a result, our proposed algorithm produces more successful results.