1. A computer implemented method for classifying a document using a plurality of confidence grades, comprising: providing a training engine and a classification engine employing a computer system for classifying said document using said plurality of confidence grades, wherein said computer system further comprises a processor, a memory unit for storing programs and data, an input/output (I/O) controller, a network interface, and a data bus; training a classifier by said training engine using a plurality of training documents, wherein training said classifier comprises: obtaining a list of first words from said training documents, wherein each of said training documents is classified into one of a plurality of classes; for each class belonging to said classes: determining a prior probability of said class, wherein said prior probability is probability of occurrence of said training documents in said class given said training documents in said classes; calculating conditional probabilities for said list of first words; calculating a minimum posterior probability and a maximum posterior probability for said class; defining multiple threshold constants by evaluating empirically said calculated minimum and maximum posterior probabilities across a number of experimentations; determining a plurality of confidence thresholds using said list of first words, said prior probability of said class, and one of said multiple threshold constants; and defining said confidence grades using said determined confidence thresholds; classifying said document by said classification engine using said trained classifier, wherein classifying said document comprises: obtaining a list of second words from said document, wherein said second words are commonly present in said list of first words; determining conditional probabilities for said list of second words for each of said classes from said calculated conditional probabilities for said list of first words; calculating a posterior probability for each of said classes using said prior probability and said determined conditional probabilities; comparing said calculated posterior probability with said determined confidence thresholds for each of said classes; assigning each of said classes to one of said defined confidence grades based on said comparison of said calculated posterior probability with said determined confidence thresholds; and assigning said document to one of said classes based on said calculated posterior probability and said assigned confidence grades for each of said classes; whereby said document is classified into one of said classes using said confidence grades.