Semiconductor production cycle is a combination of production and quality inspection steps. The collected data at these steps lead to huge amounts of data stored in heterogeneous databases. To identify yield loss' causes for a product "X", we propose a three step approach that gives as a result a set of relational patterns, corresponding to potential yield loss' causes. These patterns are generated by data mining algorithms, as associations among descriptive clusters which characterize each of the useful databases considered. First, the context identification: Using the engineering knowledge, we specify a set of the most critical production steps for the product "X" wafers. Let "Mi" be one of these sets, a succession of production "Pi" and quality inspection "Qi" steps. Second, the data analysis divided in two steps: First: cluster identification. From the dataset "dataPi", corresponding to the equipment data collected at a specific process step, we identify clusters corresponding to different operation modes of tools on this step. Besides, "dataQi" is used to identify clusters corresponding to different wafer' quality status. We propose Clustering algorithms to identify these clusters. Second: relational patterns identification between the clusters identified previously. We propose to use association rules algorithms to identify relations as "cap->dbq" between a production step' cluster "cap" and a quality measure step' cluster "dbq". The last step is interpretation: Starting from the set of association rules identified previously and with the help of specialized engineers, we can identify which of the clusters results "dbq" corresponds to a potential yield loss situation, and identify the different causes "cap" that lead to this cluster "dbq".