The non-destructive analysis of a Solid Pharmaceutical Product (SPP) is essential to verify the quality without destroying the product. This analysis may be performed using various image processing and signal processing techniques on images and multispectral data. Based on this analysis, an SPP may be classified as defective or non-defective. The SPP (categorized as defective) are exposed to three different environmental factors (humidity, temperature and moisture) over different time periods and the variations in data are analyzed to judge the effects of these factors on classification of an SPP. In this research, we have proposed two non-destructive methods to identify defective and non-defective SPPs using their surface morphology. In first approach, multiple textural features are extracted using microscopic images of the surface of the defective and non-defective SPPs. These textural features are Gray Level Co-occurrence Matrix, Run Length Matrix, Histogram, Auto Regressive Model and HAAR Wavelet. Total textural features extracted from microscopic images are 281. The features are reduced using three feature reduction techniques; Chi-square, Gain Ratio and Relief-F. We have formulated three feature sets, through experimentation, with 281, 15 and 2 features. We have used four classifiers namely Support Vector Machine, K-Nearest Neighbors, Naïve Bayes and Ensemble of Classifiers, to calculate the accuracy of proposed approach. The classifiers are implemented using leave-one-out cross validation and holdout validation methods. We tested each classifier against all feature sets and the results were compared. The results showed that in most of the cases, Support Vector Machine performed better than the other classifiers. In second approach, we have used multispectral data and applied wavelet transformations in conjunction with various machine learning techniques for the classification. The results showed that the spectrum extracted from Ultra Violet x wavelength range is more suitable for the classification between defective and non-defective SPPs. Furthermore, results also described that K-Nearest Neighbors classifier or Ensemble of Classifiers is a more appropriate classifier. In the last, the hybrid of the both approaches was tested. The analysis of the results showed that the hybrid approach is better than the individual ones. An accuracy of 94% is achieved using K-Nearest Neighbors when a combined dataset of SPPs affected by all of the three environmental factors is used.