This paper presents a meta-heuristic optimization algorithm, Tabu Search (TS), and describes how it can be used to solve a wide variety of chemical engineering problems. Modifications to the original algorithm and constraint handling techniques are described and integrated to extend its applicability. All components of TS are described in detail. Initial values for each key parameter of TS are provided. In addition, guidelines for adjusting these parameters are provided to relieve a significant amount of time-consuming trial-and-error experiments that are typically required with stochastic optimization. Several small NLP and MINLP test cases and three small-to middle-scale chemical process synthesis problems demonstrate the feasibility and effectiveness of the techniques with recommended parameters. (C) 2004 Elsevier Ltd. All rights reserved.