Picking up where Introduction to Automatic Signal Classification left off, this paper furnishes details on executing the various steps in the development of an automatic signal classifier. Topics covered include: nonlinear operations that reveal signal characteristics; graphical and statistical methods for deriving signal features; graphical and statistical methods for determining the most powerful features within computational constraints; hierarchical versus Bayesian versus neural network decision logic schemes; primary and secondary logical integration of classification decisions; and testing and performance evaluation.