diff --git a/CHANGES.md b/CHANGES.md
--- a/CHANGES.md
+++ b/CHANGES.md
@@ -1,6 +1,9 @@
 Revision history for Haskell package learning-hmm
 ===
 
+## Version 0.3.1.2
+- Default the limit of Baum-Welch iteration to 10000 (in `baumWelch'`)
+
 ## Version 0.3.1.1
 - Bug fix release
 
diff --git a/learning-hmm.cabal b/learning-hmm.cabal
--- a/learning-hmm.cabal
+++ b/learning-hmm.cabal
@@ -1,5 +1,5 @@
 name:                learning-hmm
-version:             0.3.1.1
+version:             0.3.1.2
 stability:           experimental
 
 synopsis:            Yet another library for hidden Markov models
diff --git a/src/Learning/HMM/Internal.hs b/src/Learning/HMM/Internal.hs
--- a/src/Learning/HMM/Internal.hs
+++ b/src/Learning/HMM/Internal.hs
@@ -146,12 +146,12 @@
     (models, logLs) = unzip $ iterate step (model, undefined)
 
 baumWelch' :: HMM -> U.Vector Int -> (HMM, LogLikelihood)
-baumWelch' model xs = go (undefined, -1/0) (baumWelch1 model n xs)
+baumWelch' model xs = go (10000 :: Int) (undefined, -1/0) (baumWelch1 model n xs)
   where
     n = U.length xs
-    go (m, l) (m', l')
-      | l' - l > 1.0e-9 = go (m', l') (baumWelch1 m' n xs)
-      | otherwise       = (m, l')
+    go k (m, l) (m', l')
+      | k > 0 && l' - l > 1.0e-9 = go (k - 1) (m', l') (baumWelch1 m' n xs)
+      | otherwise                = (m, l')
 
 -- | Perform one step of the Baum-Welch algorithm and return the updated
 --   model and the likelihood of the old model.
diff --git a/src/Learning/IOHMM/Internal.hs b/src/Learning/IOHMM/Internal.hs
--- a/src/Learning/IOHMM/Internal.hs
+++ b/src/Learning/IOHMM/Internal.hs
@@ -152,12 +152,12 @@
     (models, logLs) = unzip $ iterate step (model, undefined)
 
 baumWelch' :: IOHMM -> U.Vector (Int, Int) -> (IOHMM, LogLikelihood)
-baumWelch' model xys = go (undefined, -1/0) (baumWelch1 model n xys)
+baumWelch' model xys = go (10000 :: Int) (undefined, -1/0) (baumWelch1 model n xys)
   where
     n = U.length xys
-    go (m, l) (m', l')
-      | l' - l > 1.0e-9 = go (m', l') (baumWelch1 m' n xys)
-      | otherwise       = (m, l')
+    go k (m, l) (m', l')
+      | k > 0 && l' - l > 1.0e-9 = go (k - 1) (m', l') (baumWelch1 m' n xys)
+      | otherwise                = (m, l')
 
 -- | Perform one step of the Baum-Welch algorithm and return the updated
 --   model and the likelihood of the old model.