Ternary content-addressable memories (TCAMs) have gained wide acceptance in the industry for storing and searching Access Control Lists (ACLs). In this paper, we propose algorithms for addressing two important problems that are encountered while using TCAMs: reducing range expansion and multi-match classification .Our first algorithm addresses the problem of expansion of rules with range fields to represent range rules in TCAMs, a single range rule is mapped to multiple TCAM entries, which reduces the utilization of TCAMs. We propose a new scheme called Database Independent Range PreEncoding (DIRPE) that, in comparison to earlier approaches, reduces the worst-case number of TCAM entries a single rule maps on to. DIRPE works without prior knowledge of the database, scales when a large number of ranges is present, and has good incremental update properties.Our second algorithm addresses the problem of finding multiple matches in a TCAM. When searched, TCAMs return the first matching entry; however, new applications require either the first few or all matching entries. We describe a novel algorithm, called Multi-match Using Discriminators (MUD), that finds multiple matches without storing any per-search state information in the TCAM, thus making it suitable for multi-threaded environments. MUD does not increase the number of TCAM entries needed, and hence scales to large databases.Our algorithms do not require any modifications to existing TCAMs and are hence relatively easy to deploy. We evaluate the algorithms using real-life and random databases.