diff --git a/CHANGES b/CHANGES
--- a/CHANGES
+++ b/CHANGES
@@ -97,3 +97,6 @@
 
 0.2.5:
 		Added pcaN as requested by Marcel Ruegenberg
+
+0.2.5.1:
+		added pcaReduceN to PCA
diff --git a/hstatistics.cabal b/hstatistics.cabal
--- a/hstatistics.cabal
+++ b/hstatistics.cabal
@@ -1,8 +1,8 @@
 Name:               hstatistics
-Version:            0.2.5
+Version:            0.2.5.1
 License:            BSD3
 License-file:       LICENSE
-Copyright:          (c) A.V.H. McPhail 2010, 2011, 2012
+Copyright:          (c) A.V.H. McPhail 2010, 2011, 2012, 2013
 Author:             Vivian McPhail
 Maintainer:         haskell.vivian.mcphail <at> gmail <dot> com
 Stability:          provisional
@@ -16,7 +16,7 @@
      .
      Feature requests, suggestions, and bug fixes welcome.
 Category:           Math, Statistics
-tested-with:        GHC ==7.4.1
+tested-with:        GHC ==7.6.3
 
 cabal-version:      >=1.8
 
diff --git a/lib/Numeric/Statistics/PCA.hs b/lib/Numeric/Statistics/PCA.hs
--- a/lib/Numeric/Statistics/PCA.hs
+++ b/lib/Numeric/Statistics/PCA.hs
@@ -67,10 +67,11 @@
 pcaTransform d m = let d' = fmap (\x -> x - (scalar $ mean x)) d -- remove the mean from each dimension
                    in I.listArray (1,cols m) $ toRows $ (trans m) <> (fromRows $ I.elems d')
 
--- | perform a dimension-reducing PCA modification
+-- | perform a dimension-reducing PCA modification, 
+--     using an eigenvalue threshhold
 pcaReduce :: I.Array Int (Vector Double)      -- ^ the data
           -> Double                           -- ^ eigenvalue threshold
-          -> I.Array Int (Vector Double)      -- ^ the reduced data, with n principal components
+          -> I.Array Int (Vector Double)      -- ^ the reduced data
 pcaReduce d q = let u = fmap (scalar . mean) d
                     d' = zipWith (-) (I.elems d) (I.elems u)
                     cv = covarianceMatrix $ I.listArray (I.bounds d) d'
@@ -81,5 +82,20 @@
                     v = filter (\(x,_) -> x > q) v'  -- keep only eigens > than parameter
                     m = fromColumns $ snd $ unzip v
                  in I.listArray (I.bounds d) $ zipWith (+) (toRows $ m <> (trans m) <> fromRows d') (I.elems u) 
+
+-- | perform a dimension-reducing PCA modification, using N components
+pcaReduceN :: I.Array Int (Vector Double)      -- ^ the data
+           -> Int                              -- ^ N, the number of components
+           -> I.Array Int (Vector Double)      -- ^ the reduced data, with n principal components
+pcaReduceN d n = let u = fmap (scalar . mean) d
+                     d' = zipWith (-) (I.elems d) (I.elems u)
+                     cv = covarianceMatrix $ I.listArray (I.bounds d) d'
+                     (val',vec') = eigSH cv           -- the covariance matrix is real symmetric
+                     val = toList val'
+                     vec = toColumns vec'
+                     v' = zip val vec
+                     v = take n $ reverse $ sortBy (comparing fst) v'
+                     m = fromColumns $ snd $ unzip v
+                  in I.listArray (I.bounds d) $ zipWith (+) (toRows $ m <> (trans m) <> fromRows d') (I.elems u) 
 
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