We present a fingerprint matching algorithm that initially identifies the candidate common unique (minutiae)points in both the base and the input images using ratios of relative distances as the comparing function. A tree like structure is then drawn connecting the common minutiae points from bottom up in both the base and the input images. Matching score is obtained by comparing the similarity of the two tree structures based on a threshold value. We define a new term called the ?M (i) - tuple? for each minutiae point which uniquely encodes details about the local surrounding region, where i = 1 to N, and N is the number of minutiae. The proposed algorithm requires no explicit alignment of the two to-be compared fingerprint images and also tolerates distortions caused by spurious minutiae points. The algorithm is also capable of comparing and producing matching scores between two images obtained from two different kinds of sensors, hence is sensor interoperable and also reduces the FNMR in cases where there is very little overlap region between the base and the input image. We conducted evaluations on the FVC-2000 [1] datasets and have summarized the results in the concluding section.