1. A probabilistic learning element, that sequentially receives objects and outputs sequences of recognized states, said learning element comprising: means for sequentially receiving objects; means for storing, said received objects, sequences of received objects, previously learned sequences of states, states contained in said previously learned sequences of states, and predetermined types of knowledge relating to, said previously learned sequences of states, said states contained in said previously learned sequences of states, objects contained in said previously learned sequences of states, and sequences of objects contained in said previously learned sequences of states, whereby current object information relating to said received objects and said sequences of received objects is stored as well as statistical information relating to previously learned sequences of states and said states, objects and sequences of objects contained in said previously learned sequences of states; means for correlating said stored current object information with said stored statistical information for assigning probabilities to possible next states in the sequence of recognized states; means, responsive to said probabilities of possible next states, for determining a most likely next state; means, responsive to the stored current object information and statistical information, for providing a signal corresponding to the probability that a state has ended; and means, responsive to said end of state signal, for outputting said most likely next state as a recognized next state in a recognized state sequence.