Activation-spreading algorithms have recently been regaining attention in the AI and Cognitive Science communities. One form of such algorithms, those which use marker-passing, the spread of symbolic information over an associative network, have recently been shown to be useful in several different areas of AI. In this paper we review a number of the current approaches to symbolic information spread, examine a set of common issues arising from the implementation of these algorithms and describe some of the programming techniques and data structures that can be used for such tasks. The details of the implementation of one particular marker-passer are provided both to clarify some of the trade-offs inherent in the design of these algorithms and to serve as an example to those wishing to implement such systems. In addition, we also discuss the relationship of symbolic marker-passing to connectionist modeling.