To increase efficiency of automated leucocyte pattern recognition using lower feature dimensions, a novel inter-class distinctive feature selection method for chromatic leucocyte images was proposed based on attribute hierarchical relationship. According to the attribute constraints in formal concept analysis, we established a knowledge representation and discovery method based on the hierarchical optimal diagram by defining attribute value and visual representation of optimized hierarchical relationship. It was applied to human peripheral blood leucocytes classification and 12 distinctive attributes were simplified from 60 inter-class attributes, which contributes significantly to reduced feature dimensions and efficient inter-class feature classification. Compared with the classical experimental data, the inter-class distinctive feature selection method based on hierarchical optimal diagram was proved to be usable and effective for six leucocyte pattern recognition.