Machine translation outputs are not correct enough to be used as it is, except for the very simplest translations. They only give the general meaning of a sentence not the exact translation. As Machine Translation (MT) is gaining a position in the whole world, there is a need for estimating the quality of machine translation outputs. Many prominent MT-Researchers are trying to make the MT-System, that produces very good and accurate translations and that also covers maximum language pairs. If good translations out of all translations can be categorized then the time and cost can be saved to a great extent. Now, Good quality translations will be sent for post-editing and rest will be sent for pre-editing or retranslation. In this paper, Kneser Ney smoothing language model is used to calculate the probability of machine translated output. But a translation cannot be said good or bad. Based on its probability score there are many other parameters that effect its quality. The quality of machine translation is made easier to estimate for post-editing by using two different predefined famous algorithms for classification.