Automatic sheet inspection in banknote production has been used as a standard quality control tool for more than a
decade. As more and more print techniques and new security features are established, total quality in bank note printing
must be guaranteed. This aspect has a direct impact on the research and development for bank note inspection systems
in general in the sense of technological sustainability. It is accepted, that print defects are generated not only by printing
parameter changes, but also by mechanical machine parameter changes, which will change unnoticed in production.
Therefore, a new concept for a multi-sensory adaptive learning and classification model based on Fuzzy-Pattern-
Classifiers for data inspection and machine conditioning is proposed. A general aim is to improve the known inspection
techniques and propose an inspection methodology that can ensure a comprehensive quality control of the printed
substrates processed by printing presses, especially printing presses which are designed to process substrates used in the
course of the production of banknotes, security documents and others. Therefore, the research and development work in
this area necessitates a change in concept for banknote inspection in general. In this paper a new generation of FPGA
(Field Programmable Gate Array) based real time inspection technology is presented, which allows not only colour
inspection on banknote sheets, but has also the implementation flexibility for various inspection algorithms for security
features, such as window threads, embedded threads, OVDs, watermarks, screen printing etc., and multi-sensory data
processing. A variety of algorithms is described in the paper, which are designed for and implemented on FPGAs. The
focus is based on algorithmic approaches.