A computer-implemented method of aligning two data sequences of events using a single encoder neural network, a first of the data sequences being a sequence of first data items, and a second of the data sequences being a sequence of second data items, the first data items characterizing respective first events which occur in a first environment at successive first times, and the second data items characterizing respective second events which occur in a second environment at successive second times, wherein each of the data sequences are video sequences respectively showing the first and second events, the first data items and second data items each being image data captured by at least one video camera and defining at least one respective frame of the corresponding video sequence, and wherein the encoder neural network has been trained, with data sequences which each describe environments during respective periods in which an event of at least one given type occurred, to recognize that events of said at least one given type are significant, and to recognize events of said at least one given type in new data sequences, the method comprising the steps of: encoding the first data sequence with an encoder neural network, to form from each first data item a corresponding first encoded data item; encoding the second data sequence with the encoder neural network, to form from each second data item a corresponding second encoded data item; and for at least one said first data item: (i) for each of a plurality of the second data items, determining a respective distance value indicative of a distance between the corresponding first encoded data item and the corresponding second encoded data item according to a distance measure; (ii) determining one of the plurality of second data items for which the corresponding distance value is lowest; (iii) associating the first data item and the determined one of the second data items, to associate the corresponding first event with the corresponding second event, the first and second events being events of a same type; and (iv) attributing annotation data attributed to one or more data items of one of the data sequences, to respective associated data items of the other of the data sequences, wherein the annotation data explain one or more events shown in a portion of the other of the data sequences. encoding the first data sequence with an encoder neural network, to form from each first data item a corresponding first encoded data item; encoding the second data sequence with the encoder neural network, to form from each second data item a corresponding second encoded data item; and for at least one said first data item: (i) for each of a plurality of the second data items, determining a respective distance value indicative of a distance between the corresponding first encoded data item and the corresponding second encoded data item according to a distance measure; (ii) determining one of the plurality of second data items for which the corresponding distance value is lowest; (iii) associating the first data item and the determined one of the second data items, to associate the corresponding first event with the corresponding second event, the first and second events being events of a same type; and (iv) attributing annotation data attributed to one or more data items of one of the data sequences, to respective associated data items of the other of the data sequences, wherein the annotation data explain one or more events shown in a portion of the other of the data sequences. (i) for each of a plurality of the second data items, determining a respective distance value indicative of a distance between the corresponding first encoded data item and the corresponding second encoded data item according to a distance measure; (ii) determining one of the plurality of second data items for which the corresponding distance value is lowest; (iii) associating the first data item and the determined one of the second data items, to associate the corresponding first event with the corresponding second event, the first and second events being events of a same type; and (iv) attributing annotation data attributed to one or more data items of one of the data sequences, to respective associated data items of the other of the data sequences, wherein the annotation data explain one or more events shown in a portion of the other of the data sequences.