Image fusion is to reduce uncertainty and minimize redundancy. It is a process of combining the relevant information from a set of images, into a single image, wherein the resultant fused image will be more informative and complete than any of the input images. Till date the image fusion techniques were like DWT or pixel based. These conventional techniques were not that efficient and they did not produced the expected results as the edge preservance, spatial resolution and the shift invariance are the factors that could not be avoided during image fusion. This paper discusses the implementation of two  categories of image fusion. The Stationary wavelet transform (SWT), and Principal component analysis (PCA).  The Stationary wavelet transform (SWT) is a wavelet transform algorithm designed to overcome the lack of translation-invariance of the discrete wavelet transform (DWT). Whereas The Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. To overcome the disadvantages of the earlier techniques used for image fusion a new hybrid technique is proposed that works by combining the SWT and PCA i.e. stationery wavelet transform and principal component analysis. This hybrid technique is proposed to obtain a better efficient and a better quality fused image which will have preserved edges and its spatial resolution and shift invariance will be improved. This hybrid technique will produce better fusion results. The image obtained after fusion using proposed technique will be of better quality than the images fused using conventional techniques.