To be competitive in domestic and international markets, manufacturing management is routinely faced with the decision to automate or replace existing production facilities with advanced manufacturing technologies. Installation of advanced manufacturing technologies requires high capital investment, and there is much evidence to suggest that automation for automation's sake has proven to be costly and ineffective for many organizations. What makes the justification task more challenging is the lack of decision tools and methodologies that incorporate the intangible benefits from such investments. This research develops a decision support framework for modeling the evaluation and selection of manufacturing system investments. A multi-attribute decision approach is used to capture the quantitative and qualitative aspects of the decision problem. Based on data collected from slected sample from industry, this research synthesized attributes typically considered important in selection of manufacturing alternatives and established their relative importance. The effectiveness of the decision framework was evaluated using a case study, representative of an industrial application. A computer implementation of the decision framework provides a simulation model for structuring alternatives and estimating performance measures (attributes). This is integrated with a multi-attribute evaluation model for comparison of alternatives. Unlike commercial simulation systems that requires user understanding of the simulation system or language, the system developed in this research uses a natural language interface enabling its use without the need to understand the underlying programming structure. This research has two implications, practically and academically. It can assist decision makers to better understand and apply the justification process and the system behavior under different manufacturing scenarios. Academically, it can be used also as an educational tool for students to understand the justification process, and its tradeoffs and complexities.