Error recovery is an essential feature for a parser that should be plugged in Integrated Development Environments (IDEs), which must build Abstract Syntax Trees (ASTs) even for syntactically invalid programs in order to offer features such as automated refactoring and code completion. Parsing Expressions Grammars (PEGs) are a formalism that naturally describes recursive top-down parsers using a restricted form of backtracking. Labeled failures are a conservative extension of PEGs that adds an error reporting mechanism for PEG parsers, and these labels can also be associated with recovery expressions to provide an error recovery mechanism. These expressions can use the full expressivity of PEGs to recover from syntactic errors. Manually annotating a large grammar with labels and recovery expressions can be difficult. In this work, we present two approaches, Standard and Unique , to automatically annotate a PEG with labels, and to build their corresponding recovery expressions. The Standard approach annotates a grammar in a way similar to manual annotation, but it may insert labels incorrectly, while the Unique approach is more conservative to annotate a grammar and does not insert labels incorrectly. We evaluate both approaches by using them to generate error recovering parsers for four programming languages: Titan, C, Pascal and Java. In our evaluation, the parsers produced using the Standard approach, after a manual intervention to remove the labels incorrectly added, gave an acceptable recovery for at least 70% of the files in each language. By it turn, the acceptable recovery rate of the parsers produced via the Unique approach, without the need of manual intervention, ranged from 41% to 76%. • We discuss two approaches, Standard and Unique, to build PEG-based error recovering parsers in a more automatic way. • We build error recovering parsers for Titan, C, Pascal and Java. • Algorithm Standard, with the help of manual intervention, gives an acceptable recovery for at least 70% of the syntactically invalid files of each language. • Algorithm Unique, without manual intervention, gives an acceptable recovery rate that ranges from 41% to 76%.