Algorithms, applications and hardware implementations of neural networks are not investigated in close connection. Researchers working in the development of dedicated hardware implementations develop simplified versions of otherwise complex neural algorithms or develop dedicated algorithms: usually these algorithms have not been horoughly tested on real-world applications. At the same time, many theoretically sound algorithms are not feasible in dedicated hardware, therefore limiting their success only to applications where a software solution on a general-purpose system is feasible. The paper focuses on the issues related to the hardware implementation of neural algorithms and architectures and their successful application to real world-problems.