Traditional DSP is based on algorithms, changing data from one form to another through step-by-step procedures. Most of these techniques also need parameters to operate. For example: recursive filters use recursion coefficients, feature detection can be implemented by correlation and thresholds, an image display depends on the brightness and contrast settings, etc. Algorithms describe what is to be done, while parameters provide a benchmark to judge the data. The proper selection of parameters is often more important than the algorithm itself. Neural networks take this idea to the extreme by using very simple algorithms, but many highly optimized parameters. This is a revolutionary departure from the traditional mainstays of science and engineering: mathematical logic and theorizing followed by experimentation. Neural networks replace these problem solving strategies with trial & error, pragmatic solutions, and a "this works better than that" methodology. This chapter presents a variety of issues regarding parameter selection in both neural networks and more traditional DSP algorithms.