In order to study wear characteristics of a 6-cylinder diesel engine,six different working statuses were arranged by altering the clearance betweencylinder and piston. Sixty-nine oil samples were taken from engine at differentloads under 6 working statuses and analyzed by Spectroil M Instrument made in US.Principal component analysis (PCA) was applied to analyzing spectrometric data ofsixty-nine oil samples and clustering those data according to elements and oilsamples separately based on the weighted coefficient and principal componentscores. All 21 elements were used in element clustering and only 6 wear-relatedelements, namely iron, chromium, aluminum, copper, plumbum and silicon, were usedin sample clustering. It is shown that PCA effectively clustered oilspectrometric data into three different principal components according toelements. The projection of two different principal components exhibited fivetypes of elements combinations, namely wear elements (Fe, Cr, Cu, Al and Pb),high concentration additives elements (Na, Zn, P, Ca and Mg), low concentrationadditives elements (Ba and B), base constituent of lubricating oils (C and H) andinterferential elements (Ni, Ti, Mo, V, Ag and Sn). Furthermore, PCA clearlyclustered oil samples according to different clearance between cylinder andpiston in the diesel engine. The study suggests that analyzing oil spectrographicdata by PCA could find the sources of different elements,monitor engineconditions and diagnose wear faults.Diesel engine; Lubricating oil