Comparing MFCC Features ,What do they represent?
Like already said, an eye inspection in this case is not possible since the data is just to high-dimensional. Thus what you want to do is to inspect the difference magnitudes of the frequencies per time frame. 

I would suggest that you have a look at the cosine distance in this case. It's not the only way but a good possibility to compare MFCCs without distortion due to the different overall loudness of the word. The cosine distance is 1 - cosine similarity and is frequently used in text mining. In this area it might be important to be independent of the document length (document A with 100 words, 10 * "Hello" and 90 * "Bye" should be similar to document B with 200 words, 20 * "Hello" and 180 "Bye"). Another way of explaining this distance is perform a normalization step (project each data point on the unit sphere) and then measure the euclidian distance.

The cosine distance looks at the angle between two vectors (in your case you have one vector per time/frequency-bin) and therefore gets rid of the overall magnitude. This would by one way of comparing your MFCCs.