These day, face recognition became main attraction by many researcher due to demand from commercial and law enforcement sectors. The main issues in face recognition are the sensitivity toward intrinsic factors and extrinsic factors. Beside, computation time and memory usage are the important aspect to been consider. This research objective focus toward enhanced on Hybrid Multiclass Support Vector Machine (HM-SVM) model. The model consist of several phases which are data preparation, feature extraction and classification. In feature extraction phase, Discrete Wavelet Transform (DWT) and Principle Component Analysis (PCA) was applied to perform image compression, dimension reduction and feature extraction. Then, Hybrid Multiclass Support Vector Machine (HM-SVM) strategies were utilized to tackle the face recognition problem. Cambridge ORL Face Database was used as testing and training material. This database consist of 400 images of 40 individuals. The accuracy evaluation of this research was based on two different SVM kernel. Besides, the research was performed based on Orthogonal Wavelet families. Comparison was made with classic Hybrid Multiclass Support Vector Machine (HM-SVM) performance. As a result, the proposed algorithm shown the enhancement improve the classic HS-SVM approximately by 3.13-6.56 using polynomial kernel and 1.88-5.31 by using radial basis function kernel (RBF). © 2016 IEEE.