It is known that there are shortcomings in identifying uncertainties in an Ecological RiskAssessment (ERA) conclusions presented by an authority such as EFSA. In 2018, "Guidanceon Uncertainty Analysis in Scientific Assessments" was adopted by EFSA, which will assistdecision makers to identify uncertainties in the basis for decision making. What is stillmissing is a typology to ease identification of uncertainties. Therefore, finding a method tosystematically analyse EFSA conclusions to identify and topologize uncertainties has beenthe main purpose of this thesis. Key words were selected to identify uncertainties and thefrequency of use of the respective keywords in the texts was thereafter analysed. A twodimensionalALSCAL model was then used to explore the relationship between the keywords. The ALSCAL model showed that the key words uncertainty and gap were notclustered with any other keywords found in the 52 EFSA conclusions that were analysed. Themethod used proved to be valuable for creating a consistent and useful method for classifyinguncertainty however analysing a larger data set of ERA conclusions are necessary forconfirming the accuracy and usefulness of the classification method.