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This thesis explains business process mining and it's principles. A substantial part is devoted to the problems of process discovery. Further, based on the analysis of specific manufacturing process are proposed three methods that are trying to identify shortcomings in the process. First discovers the manufacturing process and renders it into a graph. The second method uses simulator of production history to obtain products that may caused delays in the process. Acquired data are used to mine frequent itemsets. The third method tries to predict processing time on the selected workplace using asociation rules. Last two mentioned methods employ an algorithm Frequent Pattern Growth. The knowledge obtained from this thesis improve efficiency of the manufacturing process and enables better production planning.