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Optimisation and automation of determination of cutting conditions in operation planning depend significantly on availability of reliable machinability data and knowledge. In order to improve and automate the tool selection and determination of cutting parameters in operation planning we have to re-formulate and generalise the existing machinability knowledge. In the paper the existent machinability data base was analysed by the use of machine learning methodology. A multi-stage experiment has been carried out, comprising (1) preparatory phase in which manual construction of higher level attributes and grouping of similar learning examples to obtain more consistent decision trees was performed, and (2) learning relations between workpiece materials to be machined, cutting tool features and cutting conditions. Within the learning process several decision trees have been synthesised predicting tool features …