1. A method for on-line handwriting recognition based on a hidden Markov model, said method comprising the steps of: real-time sensing at least an instantaneous write position of said handwriting; deriving from said handwriting a time-conforming string of samples, and from said string a series of handwriting intervals each associated by derivation to a handwriting feature vector, including calculating a center of gravity from the samples associated with each handwriting interval, and wherein the feature vectors contain one or more vector elements that are based on local observations derived from a single said handwriting interval, said handwriting interval comprising a handwriting frame, as well as being based on one or more vector elements that are based on aggregated observations derived from time-spaced said intervals defined over an associated delay, wherein the aggregated feature vector elements are representable as o'.sub.1 and o'.sub.2 as follows: ##EQU2## where o.sub.1 and o.sub.2 represent the vector elements that are based on local observations, d corresponds to a maximum value of the delay between successive frames, and m indicates a number of functions actually used; matching the time-conforming sequence of feature vectors so derived to one or more example sequence modellings from a database pertaining to the handwriting in question; selecting from said modellings a sufficiently-matching recognition string through hidden-Markov processing; and outputting essentials of a result of said selecting, or alternatively, rejecting said handwriting as failing to be recognized.
4. A method as claimed in claim 1, wherein said aggregated feature vector elements define spatially contiguous character segments generated in a temporally non-sequential manner.