To uncover dominant kinds of factors that describe as much data as possible, principal components are computed directly from the SVD of mean subtracted data. A single value in sigma describes the amount of variance of a data set that the principal component captures. The higher influencing factors on principal component 1 are tip-to-collector distance and, dope injection rate, accounting for nearly 80% of its weight. The higher influencing factors on principal component 2 are high voltage (kV) and liquid entry pressure (kPa), accounting for nearly 70% of its weight. Both components showed the same variance. The final results indicated that the factors mainly affecting the morphology of nanofibers are: high voltage (kV), liquid entry pressure (kPa), tip-to-collector distance (cm), and dope injection rate.