We　present　a　method　based　on　an　electrochemical　sensor　array　and　a　back　propagation　artificial　neural　network　for　detection　and　quantification　of　four　properties　of　lubrication　oil,　namely　water　(0,　500　ppm,　1000　ppm),　total　acid　number　(TAN)　(13.1,　13.7,　14.4,　15.6　mg　KOH　g(-1)),　soot　(0,　1%,　2%,　3%)　and　sulfur　content　(1.3%,　1.37%,　1.44%,　1.51%).　The　sensor　array,　consisting　of　four　micromachined　electrochemical　sensors,　detects　the　four　properties　with　overlapping　sensitivities.　A　total　set　of　36　oil　samples　containing　mixtures　of　water,　soot,　and　sulfuric　acid　with　different　concentrations　were　prepared　for　testing.　The　sensor　array＇s　responses　were　then　divided　to　three　sets:　training　sets　(80%　data),　validation　sets　(10%)　and　testing　sets　(10%).　Several　back　propagation　artificial　neural　network　architectures　were　trained　with　the　training　and　validation　sets;　one　architecture　with　four　input　neurons,　50　and　5　neurons　in　the　first　and　second　hidden　layer,　and　four　neurons　in　the　output　layer　was　selected.　The　selected　neural　network　was　then　tested　using　the　four　sets　of　testing　data　(10%).　Test　results　demonstrated　that　the　developed　artificial　neural　network　is　able　to　quantitatively　determine　the　four　lubrication　properties　(water,　TAN,　soot,　and　sulfur　content)　with　a　maximum　prediction　error　of　18.8%,　6.0%,　6.7%,　and　5.4%,　respectively,　indicting　a　good　match　between　the　target　and　predicted　values.　With　the　developed　network,　the　sensor　array　could　be　potentially　used　for　online　lubricant　oil　condition　monitoring.