This paper addresses the problem of performing diagnosis using production test results in a test compression environment. For linear response compactors, such as multiple-input shift register (MISRs), diagnosis must be performed from signatures. The key idea in this work is to use symbolic canceling in MISR signatures to extract information from both the MISR signature bits with errors as well as those that are error-free to provide more precise diagnostic information. A fundamentally new technique for precisely identifying error locations for propagation cones reaching fewer scan cells than the size of the MISR is described. The proposed approach does not require any additional hardware or extra data to be collected. It uses off-line software-based processing (symbolic simulation combined with Gaussian elimination) to extract information from signatures to deduce error locations even when there are a large number of errors. Unlike existing techniques for diagnosis from signatures, the proposed diagnosis approach can be used even when there are unknown (X) values in the output response. Experimental results demonstrate the reductions in suspect set size that can be obtained with the proposed techniques.