A method of monitoring a rail network comprising: taking a first data set corresponding to measurement signals from a plurality of channels of at least one fibre optic distributed acoustic sensor having a sensing fibre deployed to monitor at least part of the path of the rail network, wherein the first data set corresponds to measurement signals acquired as a train passed along a first monitored section of the rail network; and identifying a speed of the train through the first monitored section; characterized by : dividing the first data set into a plurality of time windows , each time window comprising a different subset of the first data set, wherein each time window comprises measurement signals from the plurality of channels, the measurement signal for each successive channel in a time window being delayed with respect to the previous channel by a time related to the identified train speed; for each time window, identifying and applying any time shift to the measurement signals for a channel so as to align the measurement signals of the channels within the time window; combining the time windows after any time shifts have been applied to form an aligned first data set; and deriving a characteristic train signal from the aligned first data set. taking a first data set corresponding to measurement signals from a plurality of channels of at least one fibre optic distributed acoustic sensor having a sensing fibre deployed to monitor at least part of the path of the rail network, wherein the first data set corresponds to measurement signals acquired as a train passed along a first monitored section of the rail network; and identifying a speed of the train through the first monitored section; characterized by : dividing the first data set into a plurality of time windows , each time window comprising a different subset of the first data set, wherein each time window comprises measurement signals from the plurality of channels, the measurement signal for each successive channel in a time window being delayed with respect to the previous channel by a time related to the identified train speed; for each time window, identifying and applying any time shift to the measurement signals for a channel so as to align the measurement signals of the channels within the time window; combining the time windows after any time shifts have been applied to form an aligned first data set; and deriving a characteristic train signal from the aligned first data set. dividing the first data set into a plurality of time windows , each time window comprising a different subset of the first data set, wherein each time window comprises measurement signals from the plurality of channels, the measurement signal for each successive channel in a time window being delayed with respect to the previous channel by a time related to the identified train speed; for each time window, identifying and applying any time shift to the measurement signals for a channel so as to align the measurement signals of the channels within the time window; combining the time windows after any time shifts have been applied to form an aligned first data set; and deriving a characteristic train signal from the aligned first data set. <EOS>