Sensitivity to noises and outliers and inferior robustness are the primary problems associated with extreme learning machine(ELM).Based on the 1-norm loss function,a truncated 1-norm loss function is proposed to suppress the effects of noises and outliers.A robust ELM model with truncated 1-norm loss function is established.The corresponding optimization problem is solved by iterative re-weighted algorithm.Four simulated data sets and nine real-world data sets are used to verify the validity of the proposed model.The numerical results show that the generalization performance of robust ELM in noisy environment is superior to that of the compared methods and has superior robustness,especially in the case of a substantial proportion of outliers.