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This chapter reveals how ISO normalization guidelines can have misleading recommendations, explains existing objections to descriptive approaches to normalization, and suggests a method that draws upon advances in stochastic multi-attribute analysis (SMAA) to resolve some of the most difficult challenges associated with LCA, such as eliciting criteria weights and understanding the uncertainty of those weights relative to other data. External normalization is unsuitable for comparative LCA, because it derives from normative theories that use an absolute scale and assume transitivity. Impact assessment in comparative LCAs would benefit from the application of descriptive approaches extant in Multi-Criteria Decision Analysis (MCDA) to help structure normalization and weighting stages. Specifically, outranking MCDA methods allow for the comparison of multiple competing alternatives by only allowing partial compensation. It is essential to provide robust methods for comparative LCAs that are sensitive to inherent uncertainties and capable of representing multiple viewpoints.