State-space　(SS)　model　is　proposed　to　identify　the　time　lag　between　the　asynchronous　accelerations　at　different　locations　of　the　Jiangyin　Bridge　measured　during　a　ship-bridge　collision.　One　of　the　accelerations　is　chosen　as　the　reference　signal,　and　the　time　axis　of　the　rest　of　them　are　shifted　relative　to　that　of　the　reference　signal　with　a　series　of　shifting　times.　For　each　pair　of　reference　and　time　shifted　signals,　SS　models　in　correspondence　to　the　shifting　time　series　are　formulated.　Their　system　matrices　are　identified　with　data-driven　stochastic　subspace　identification　algorithm,　and　their　model　order　is　determined　by　Akaike＇s　information　theoretic　criterion　and　final　prediction　error.　If　the　2　accelerations　for　model　fitting　are　asynchronous,　errors　may　be　introduced　into　the　SS　model　and　its　prediction　error　is　expected　to　be　greater　than　the　counterpart　obtained　with　synchronous　accelerations.　Therefore,　the　actual　time　lag　between　them　is　identified　from　the　shifting　time　that　corresponds　to　the　minimum　of　loss　function,　which　is　used　to　map　the　prediction　error　vector　sequence　to　a　real　number.　In　addition,　asynchronous　acceleration　data　measured　2　hr　ahead　of　the　ship-bridge　collision　and　synchronous　acceleration　data　measured　long　after　the　ship-bridge　collision　are　also　analyzed.　The　former　dataset　is　exploited　to　evaluate　the　reproducibility　of　the　SS　model　for　time　synchronization,　and　the　latter　dataset　is　utilized　to　examine　its　anti-false-identification　capability.　The　results　show　that　the　SS　model　achieves　a　satisfactory　performance　in　the　identification　of　time　lag　for　both　asynchronous　and　synchronous　measurement　data.