This study proposes a hybrid model for evaluating surface roughness in hard turning using a Bayesian inference-based hidden Markov model and least squares support vector machine (HMM-SVM). The model inputs are multidirectional fusion features that are extracted from the acquired monitoring signals through independent component analysis and singular spectrum analysis. Based on a detailed analysis of the workpiece surface formation mechanism, the cutting vibration signals are determined as monitoring signals and an experimental scheme based on the multifeed rate is designed. The error rate of HMM-SVM is further reduced by introducing the stratification factor comparison method rather than using the conventional probability comparison method. A five-step iterative algorithm is presented to select and optimize the training set, which effectively solves the problems of precision degradation and training data insufficiency. Experimental studies show that the proposed model can accurately predict the surface roughness in case of missing samples. The advantages of the proposed model over least squares support vector machine (LSSVM) and multiple regression approaches are demonstrated via statistical analysis.