Data-mining is aimed at discovering knowledge from data. Advanced data-mining software, such as Weka, Orange and Rapid Miner, provide hundreds of data-mining methods. In order to perform a given data-mining task, these methods have to be selected and combined into a data-mining workflow. Traditionally, workflows are designed by data-mining experts, but this is difficult and there is a strong need to automate workflow design. In doing so, it is essential to be able to assess the quality of workflows. So far, this was usually assessed only through performance indicators, such as classification accuracy. In this paper, we present a workflow assessment model that uses an extended set of user-oriented indicators, which include understandability for the user, generality of used components, and robustness of the workflow. The model, which was developed using software DEXi, is qualitative, multi-attribute, hierarchical, and rule-based. We describe its components, current implementation of the model, and illustrate its performance on the case of two workflows.