Micro-droplet　generation　is　related　to　liquid　dispensing　technology　that　has　potential　applications　in　many　fields.　Specifically,　pneumatic　micro-droplet　generation　is　controlled　by　a　solenoid　valve　being　briefly　turned　on,　so　that　high　pressure　gas　enters　the　liquid　reservoir,　forming　a　gas　pressure　pulse　waveform　P(t),　forcing　the　liquid　out　through　a　tiny　nozzle　to　form　a　micro-droplet.　For　each　ejection,　P(t)　is　acquired　by　a　high　speed　pressure　sensor,　and　the　ejection　state　is　obtained　by　machine　vision　methods.　A　prediction　model　based　on　BP　neural　network　is　established,　with　P(t)　as　input　and　the　droplet　ejection　state　as　output.　Experiments　show　that　the　BP　neural　network　can　predict　the　number　of　droplets　with　an　accuracy　higher　than　99%.　It　is　also　shown　that　the　BP　neural　network　can　improve　the　prediction　accuracy　for　the　position　of　droplets　relative　to　the　nozzle,　at　a　given　moment.　Under　typical　working　conditions,　P(t)　is　not　consistent.　As　a　result,　the　ejection　state　is　not　consistent　either.　These　prediction　models　may　be　used　for　real　time　monitoring　and　control　of　the　pneumatic　micro-droplet　generator.