The increased use of fingerprint recognition systems has brought the issue of fingerprint sensor interoperability to the forefront. Fingerprint sensor interoperability refers to the process of matching fingerprints collected from different sensors. Variability in the fingerprint image is introduced due to the differences in acquisition technology and interaction with the sensor. The effect of sensor interoperability on performance of minutiae based matchers is examined in this dissertation. Fingerprints from 190 participants were collected on nine different fingerprint sensors which included optical, capacitive, and thermal acquisition technologies and touch, and swipe interaction types. The NBIS and VeriFinger 5.0 feature extractor and matcher were used. Along with fingerprints, characteristics like moisture content, oiliness, elasticity and temperature of the skin were also measured. A statistical analysis framework for testing interoperability was formulated for this dissertation, which included parametric and non-parametric tests. The statistical analysis framework tested similarity of minutiae count, image quality and similarity of performance between native and interoperable datasets. False non-match rate (FNMR) was used as the performance metric in this dissertation. Interoperability performance analysis was conducted on each sensor dataset and also by grouping datasets based on the acquisition technology and interaction type of the acquisition sensor. Similarity of minutiae count and image quality scores between two datasets was not an indicator of similarity of FNMR for their interoperable datasets. Interoperable FNMR of 1.47% at fixed FMR of 0.1% was observed for the optical touch and capacitive touch groupings. The impact of removing low quality fingerprint images on the effect of interoperable FNMR was also examined. Although the absolute value of FNMR reduced for all the datasets, fewer interoperable datasets were found to be statistically similar to the native datasets. An image transformation method was also proposed to compensate for the differences in the fingerprint images between two datasets, and experiments conducted using this method showed significant reduction in interoperable FNMR using the transformed dataset.