1. A probabilistic learning system that receives sequential input data and outputs sequences of recognized patterns, comprising: an array of interconnected probabilistic learning elements, each element sequentially receiving objects and outputting sequences of recognized states, each element including, 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 statisitcal 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 probability signal, for outputting said most likely next state as a recognized next state in a recognized state sequence, said array of learning elements being interconnected to have a number of input learning elements and a number of output learning elements, the recognized state sequences of predetermined learning elements being combined to form objects to be received by other learning elements in the array; means for receiving and partitioning the input data between the input learning elements of the array in an overlapping and redundant manner, whereby the partitioned input data become objects provided to the input learning elements; and means for collecting and combining the recognized state sequences from the output learning elements of the array and for providing a sequence of recognized patterns as an output of the probabilistic learning system, whereby the reliability of the learning system is enhanced due to the overlapping and redundant nature in which the input data are processed through the system and the time required to perform the system task is reduced through the use of parallel processing through the array.