Fingerprint identification or verification is used more often in civilian applications. In the near future, Automatic Fingerprint Identification Systems (AFIS) can be found on mobile phones or smartcards. Most AFIS are however computationally intensive, and are designed for execution on large systems such as PC's. The main bottleneck is the minutiae extraction part. In this thesis a solution is presented to perform this part on a FPGA. The solution consists of a modified version of the MINDTCT system, designed by NIST. It runs on an open-source micro-controller called LEON2, that is created by Gaisler. Both the software and hardware are modified to form a well operating minutiae extractor. The software is stripped and fixed-point conversion is performed. Also some algorithms are modified to enhance performance. For the LEON the right configuration needed to be found. The most time consuming parts of the software are accelerated by using co-processors. They where connected to the micro-controller, by using the CPI interface. This project shows it is possible to run a fingerprint minutiae extractor on a FPGA, while using limited resources. Because the software is very computationally intensive, it takes an average of 60 seconds to complete one fingerprint. The accelerators will speed-up the system by almost 40%. The solution is based on the System-on-Chip (SoC) principle and therefore provides a perfect basis for a low-power fingerprint chip.