Near infrared (NIR) spectroscopy has been used in a noninvasively mode to develop qualitative and quantitative methods for the monitoring of a wet granulation process. The formulation contained API (10%w/w) and microcrystalline cellulose and maize starch as main excipients. NIR spectra have been acquired through the glass window of the fluidizer in reflectance mode without causing interference to neither the process nor the formulation. The spectral data has been used to develop a qualitative multivariate model based on principal component analysis (PCA). This qualitative model allows the monitoring of different steps during the granulation process only using the spectral data. Also, a quantitative calibration model based on partial least squares (PLS) methodology has been obtained to predict relevant parameters of the process, such as the moisture content, particle size distribution, and bulk density. The methodology for data acquisition, calibration modeling and method application is relatively low-cost and can be easily performed on most of the pharmaceutical sites. Based on the results, the proposed strategy provides excellent results for the monitoring of granulation processes in the pharmaceutical industry.